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2305.18724
|
2023-05-30T04:03:15Z
|
Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal
Transformer
|
[
"Yang Zhang",
"Lingbo Liu",
"Xinyu Xiong",
"Guanbin Li",
"Guoli Wang",
"Liang Lin"
] |
Wind power is attracting increasing attention around the world due to its
renewable, pollution-free, and other advantages. However, safely and stably
integrating the high permeability intermittent power energy into electric power
systems remains challenging. Accurate wind power forecasting (WPF) can
effectively reduce power fluctuations in power system operations. Existing
methods are mainly designed for short-term predictions and lack effective
spatial-temporal feature augmentation. In this work, we propose a novel
end-to-end wind power forecasting model named Hierarchical Spatial-Temporal
Transformer Network (HSTTN) to address the long-term WPF problems.
Specifically, we construct an hourglass-shaped encoder-decoder framework with
skip-connections to jointly model representations aggregated in hierarchical
temporal scales, which benefits long-term forecasting. Based on this framework,
we capture the inter-scale long-range temporal dependencies and global spatial
correlations with two parallel Transformer skeletons and strengthen the
intra-scale connections with downsampling and upsampling operations. Moreover,
the complementary information from spatial and temporal features is fused and
propagated in each other via Contextual Fusion Blocks (CFBs) to promote the
prediction further. Extensive experimental results on two large-scale
real-world datasets demonstrate the superior performance of our HSTTN over
existing solutions.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.18774
|
2023-05-30T06:17:35Z
|
Bayesian Decision Trees Inspired from Evolutionary Algorithms
|
[
"Efthyvoulos Drousiotis",
"Alexander M. Phillips",
"Paul G. Spirakis",
"Simon Maskell"
] |
Bayesian Decision Trees (DTs) are generally considered a more advanced and
accurate model than a regular Decision Tree (DT) because they can handle
complex and uncertain data. Existing work on Bayesian DTs uses Markov Chain
Monte Carlo (MCMC) with an accept-reject mechanism and sample using naive
proposals to proceed to the next iteration, which can be slow because of the
burn-in time needed. We can reduce the burn-in period by proposing a more
sophisticated way of sampling or by designing a different numerical Bayesian
approach. In this paper, we propose a replacement of the MCMC with an
inherently parallel algorithm, the Sequential Monte Carlo (SMC), and a more
effective sampling strategy inspired by the Evolutionary Algorithms (EA).
Experiments show that SMC combined with the EA can produce more accurate
results compared to MCMC in 100 times fewer iterations.
|
[
"cs.LG",
"cs.NE"
] | false |
2305.18780
|
2023-05-30T06:24:50Z
|
Who Would be Interested in Services? An Entity Graph Learning System for
User Targeting
|
[
"Dan Yang",
"Binbin Hu",
"Xiaoyan Yang",
"Yue Shen",
"Zhiqiang Zhang",
"Jinjie Gu",
"Guannan Zhang"
] |
With the growing popularity of various mobile devices, user targeting has
received a growing amount of attention, which aims at effectively and
efficiently locating target users that are interested in specific services.
Most pioneering works for user targeting tasks commonly perform
similarity-based expansion with a few active users as seeds, suffering from the
following major issues: the unavailability of seed users for newcoming services
and the unfriendliness of black-box procedures towards marketers. In this
paper, we design an Entity Graph Learning (EGL) system to provide explainable
user targeting ability meanwhile applicable to addressing the cold-start issue.
EGL System follows the hybrid online-offline architecture to satisfy the
requirements of scalability and timeliness. Specifically, in the offline stage,
the system focuses on the heavyweight entity graph construction and user entity
preference learning, in which we propose a Three-stage Relation Mining
Procedure (TRMP), breaking loose from the expensive seed users. At the online
stage, the system offers the ability of user targeting in real-time based on
the entity graph from the offline stage. Since the user targeting process is
based on graph reasoning, the whole process is transparent and
operation-friendly to marketers. Finally, extensive offline experiments and
online A/B testing demonstrate the superior performance of the proposed EGL
System.
|
[
"cs.LG",
"cs.IR"
] | false |
2305.18811
|
2023-05-30T07:57:05Z
|
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time
Series
|
[
"Wenjie Du"
] |
PyPOTS is an open-source Python library dedicated to data mining and analysis
on multivariate partially-observed time series, i.e. incomplete time series
with missing values, A.K.A. irregularlysampled time series. Particularly, it
provides easy access to diverse algorithms categorized into four tasks:
imputation, classification, clustering, and forecasting. The included models
contain probabilistic approaches as well as neural-network methods, with a
well-designed and fully-documented programming interface for both academic
researchers and industrial professionals to use. With robustness and
scalability in its design philosophy, best practices of software construction,
for example, unit testing, continuous integration (CI) and continuous delivery
(CD), code coverage, maintainability evaluation, interactive tutorials, and
parallelization, are carried out as principles during the development of
PyPOTS. The toolkit is available on both Python Package Index (PyPI) and
Anaconda. PyPOTS is open-source and publicly available on GitHub
https://github.com/WenjieDu/PyPOTS.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.18818
|
2023-05-30T08:07:41Z
|
Shapley Based Residual Decomposition for Instance Analysis
|
[
"Tommy Liu",
"Amanda Barnard"
] |
In this paper, we introduce the idea of decomposing the residuals of
regression with respect to the data instances instead of features. This allows
us to determine the effects of each individual instance on the model and each
other, and in doing so makes for a model-agnostic method of identifying
instances of interest. In doing so, we can also determine the appropriateness
of the model and data in the wider context of a given study. The paper focuses
on the possible applications that such a framework brings to the relatively
unexplored field of instance analysis in the context of Explainable AI tasks.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.18845
|
2023-05-30T08:36:43Z
|
How Generative Models Improve LOS Estimation in 6G Non-Terrestrial
Networks
|
[
"Saira Bano",
"Achilles Machumilane",
"Pietro Cassarà",
"Alberto Gotta"
] |
With the advent of 5G and the anticipated arrival of 6G, there has been a
growing research interest in combining mobile networks with Non-Terrestrial
Network platforms such as low earth orbit satellites and Geosynchronous
Equatorial Orbit satellites to provide broader coverage for a wide range of
applications. However, integrating these platforms is challenging because
Line-Of-Sight (LOS) estimation is required for both inter satellite and
satellite-to-terrestrial segment links. Machine Learning (ML) techniques have
shown promise in channel modeling and LOS estimation, but they require large
datasets for model training, which can be difficult to obtain. In addition,
network operators may be reluctant to disclose their network data due to
privacy concerns. Therefore, alternative data collection techniques are needed.
In this paper, a framework is proposed that uses generative models to generate
synthetic data for LOS estimation in non-terrestrial 6G networks. Specifically,
the authors show that generative models can be trained with a small available
dataset to generate large datasets that can be used to train ML models for LOS
estimation. Furthermore, since the generated synthetic data does not contain
identifying information of the original dataset, it can be made publicly
available without violating privacy
|
[
"cs.NI",
"cs.LG",
"C.2.3"
] | false |
2305.19041
|
2023-05-30T13:58:13Z
|
NicePIM: Design Space Exploration for Processing-In-Memory DNN
Accelerators with 3D-Stacked-DRAM
|
[
"Junpeng Wang",
"Mengke Ge",
"Bo Ding",
"Qi Xu",
"Song Chen",
"Yi Kang"
] |
With the widespread use of deep neural networks(DNNs) in intelligent systems,
DNN accelerators with high performance and energy efficiency are greatly
demanded. As one of the feasible processing-in-memory(PIM) architectures,
3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory
and low-cost memory access, which is a promising solution for DNN accelerators
with better performance and energy efficiency. However, the low-cost
characteristics of stacked DRAM and the distributed manner of memory access and
data storing require us to rebalance the hardware design and DNN mapping. In
this paper, we propose NicePIM to efficiently explore the design space of
hardware architecture and DNN mapping of DRAM-PIM accelerators, which consists
of three key components: PIM-Tuner, PIM-Mapper and Data-Scheduler. PIM-Tuner
optimizes the hardware configurations leveraging a DNN model for classifying
area-compliant architectures and a deep kernel learning model for identifying
better hardware parameters. PIM-Mapper explores a variety of DNN mapping
configurations, including parallelism between branches of DNN, DNN layer
partitioning, DRAM capacity allocation and data layout pattern in DRAM to
generate high-hardware-utilization DNN mapping schemes for various hardware
configurations. The Data-Scheduler employs an integer-linear-programming-based
data scheduling algorithm to alleviate the inter-PIM-node communication
overhead of data-sharing brought by DNN layer partitioning. Experimental
results demonstrate that NicePIM can optimize hardware configurations for
DRAM-PIM systems effectively and can generate high-quality DNN mapping schemes
with latency and energy cost reduced by 37% and 28% on average respectively
compared to the baseline method.
|
[
"cs.AR",
"cs.LG"
] | false |
2305.19076
|
2023-05-30T14:40:39Z
|
Class Conditional Gaussians for Continual Learning
|
[
"Thomas L. Lee",
"Amos Storkey"
] |
Dealing with representation shift is one of the main problems in online
continual learning. Current methods mainly solve this by reducing
representation shift, but leave the classifier on top of the representation to
slowly adapt, in many update steps, to the remaining representation shift,
increasing forgetting. We propose DeepCCG, an empirical Bayesian approach to
solve this problem. DeepCCG works by updating the posterior of a class
conditional Gaussian classifier such that the classifier adapts instantly to
representation shift. The use of a class conditional Gaussian classifier also
enables DeepCCG to use a log conditional marginal likelihood loss to update the
representation, which can be seen as a new type of replay. To perform the
update to the classifier and representation, DeepCCG maintains a fixed number
of examples in memory and so a key part of DeepCCG is selecting what examples
to store, choosing the subset that minimises the KL divergence between the true
posterior and the posterior induced by the subset. We demonstrate the
performance of DeepCCG on a range of settings, including those with overlapping
tasks which thus far have been under-explored. In the experiments, DeepCCG
outperforms all other methods, evidencing its potential.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19161
|
2023-05-30T16:05:44Z
|
Cooperative Thresholded Lasso for Sparse Linear Bandit
|
[
"Haniyeh Barghi",
"Xiaotong Cheng",
"Setareh Maghsudi"
] |
We present a novel approach to address the multi-agent sparse contextual
linear bandit problem, in which the feature vectors have a high dimension $d$
whereas the reward function depends on only a limited set of features -
precisely $s_0 \ll d$. Furthermore, the learning follows under
information-sharing constraints. The proposed method employs Lasso regression
for dimension reduction, allowing each agent to independently estimate an
approximate set of main dimensions and share that information with others
depending on the network's structure. The information is then aggregated
through a specific process and shared with all agents. Each agent then resolves
the problem with ridge regression focusing solely on the extracted dimensions.
We represent algorithms for both a star-shaped network and a peer-to-peer
network. The approaches effectively reduce communication costs while ensuring
minimal cumulative regret per agent. Theoretically, we show that our proposed
methods have a regret bound of order $\mathcal{O}(s_0 \log d + s_0 \sqrt{T})$
with high probability, where $T$ is the time horizon. To our best knowledge, it
is the first algorithm that tackles row-wise distributed data in sparse linear
bandits, achieving comparable performance compared to the state-of-the-art
single and multi-agent methods. Besides, it is widely applicable to
high-dimensional multi-agent problems where efficient feature extraction is
critical for minimizing regret. To validate the effectiveness of our approach,
we present experimental results on both synthetic and real-world datasets.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19183
|
2023-05-30T16:27:25Z
|
Graph-based Time Series Clustering for End-to-End Hierarchical
Forecasting
|
[
"Andrea Cini",
"Danilo Mandic",
"Cesare Alippi"
] |
Existing relationships among time series can be exploited as inductive biases
in learning effective forecasting models. In hierarchical time series,
relationships among subsets of sequences induce hard constraints (hierarchical
inductive biases) on the predicted values. In this paper, we propose a
graph-based methodology to unify relational and hierarchical inductive biases
in the context of deep learning for time series forecasting. In particular, we
model both types of relationships as dependencies in a pyramidal graph
structure, with each pyramidal layer corresponding to a level of the hierarchy.
By exploiting modern - trainable - graph pooling operators we show that the
hierarchical structure, if not available as a prior, can be learned directly
from data, thus obtaining cluster assignments aligned with the forecasting
objective. A differentiable reconciliation stage is incorporated into the
processing architecture, allowing hierarchical constraints to act both as an
architectural bias as well as a regularization element for predictions.
Simulation results on representative datasets show that the proposed method
compares favorably against the state of the art.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19194
|
2023-05-30T16:39:11Z
|
FakeSwarm: Improving Fake News Detection with Swarming Characteristics
|
[
"Jun Wu",
"Xuesong Ye"
] |
The proliferation of fake news poses a serious threat to society, as it can
misinform and manipulate the public, erode trust in institutions, and undermine
democratic processes. To address this issue, we present FakeSwarm, a fake news
identification system that leverages the swarming characteristics of fake news.
To extract the swarm behavior, we propose a novel concept of fake news swarming
characteristics and design three types of swarm features, including principal
component analysis, metric representation, and position encoding. We evaluate
our system on a public dataset and demonstrate the effectiveness of
incorporating swarm features in fake news identification, achieving an f1-score
and accuracy of over 97% by combining all three types of swarm features.
Furthermore, we design an online learning pipeline based on the hypothesis of
the temporal distribution pattern of fake news emergence, validated on a topic
with early emerging fake news and a shortage of text samples, showing that
swarm features can significantly improve recall rates in such cases. Our work
provides a new perspective and approach to fake news detection and highlights
the importance of considering swarming characteristics in detecting fake news.
|
[
"cs.SI",
"cs.LG"
] | false |
2305.19211
|
2023-05-30T17:01:53Z
|
COVID-19 Detection from Mass Spectra of Exhaled Breath
|
[
"Nicolò Bellarmino",
"Giorgio Bozzini",
"Riccardo Cantoro",
"Francesco Castelletti",
"Michele Castelluzzo",
"Carla Ciricugno",
"Raffaele Correale",
"Daniela Dalla Gasperina",
"Francesco Dentali",
"Giovanni Poggialini",
"Piergiorgio Salerno",
"Giovanni Squillero",
"Stefano Taborelli"
] |
According to the World Health Organization, the SARS-CoV-2 virus generated a
global emergency between 2020 and 2023 resulting in about 7 million deaths out
of more than 750 million individuals diagnosed with COVID-19. During these
years, polymerase-chain-reaction and antigen testing played a prominent role in
disease control. In this study, we propose a fast and non-invasive detection
system exploiting a proprietary mass spectrometer to measure ions in exhaled
breath. We demonstrated that infected individuals, even if asymptomatic,
exhibit characteristics in the air expelled from the lungs that can be detected
by a nanotech-based technology and then recognized by soft-computing
algorithms. A clinical trial was ran on about 300 patients: the mass spectra in
the 10-351 mass-to-charge range were measured, suitably pre-processed, and
analyzed by different classification models; eventually, the system shown an
accuracy of 95% and a recall of 94% in identifying cases of COVID-19. With
performances comparable to traditional methodologies, the proposed system could
play a significant role in both routine examination for common diseases and
emergency response for new epidemics.
|
[
"cs.LG",
"q-bio.QM"
] | false |
2305.19218
|
2023-05-30T17:05:49Z
|
Adversarial Attacks on Online Learning to Rank with Stochastic Click
Models
|
[
"Zichen Wang",
"Rishab Balasubramanian",
"Hui Yuan",
"Chenyu Song",
"Mengdi Wang",
"Huazheng Wang"
] |
We propose the first study of adversarial attacks on online learning to rank.
The goal of the adversary is to misguide the online learning to rank algorithm
to place the target item on top of the ranking list linear times to time
horizon $T$ with a sublinear attack cost. We propose generalized list poisoning
attacks that perturb the ranking list presented to the user. This strategy can
efficiently attack any no-regret ranker in general stochastic click models.
Furthermore, we propose a click poisoning-based strategy named attack-then-quit
that can efficiently attack two representative OLTR algorithms for stochastic
click models. We theoretically analyze the success and cost upper bound of the
two proposed methods. Experimental results based on synthetic and real-world
data further validate the effectiveness and cost-efficiency of the proposed
attack strategies.
|
[
"cs.LG",
"cs.CR"
] | false |
2305.19244
|
2023-05-30T17:32:00Z
|
Testing for the Markov Property in Time Series via Deep Conditional
Generative Learning
|
[
"Yunzhe Zhou",
"Chengchun Shi",
"Lexin Li",
"Qiwei Yao"
] |
The Markov property is widely imposed in analysis of time series data.
Correspondingly, testing the Markov property, and relatedly, inferring the
order of a Markov model, are of paramount importance. In this article, we
propose a nonparametric test for the Markov property in high-dimensional time
series via deep conditional generative learning. We also apply the test
sequentially to determine the order of the Markov model. We show that the test
controls the type-I error asymptotically, and has the power approaching one.
Our proposal makes novel contributions in several ways. We utilize and extend
state-of-the-art deep generative learning to estimate the conditional density
functions, and establish a sharp upper bound on the approximation error of the
estimators. We derive a doubly robust test statistic, which employs a
nonparametric estimation but achieves a parametric convergence rate. We further
adopt sample splitting and cross-fitting to minimize the conditions required to
ensure the consistency of the test. We demonstrate the efficacy of the test
through both simulations and the three data applications.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19268
|
2023-05-30T17:58:49Z
|
Intriguing Properties of Quantization at Scale
|
[
"Arash Ahmadian",
"Saurabh Dash",
"Hongyu Chen",
"Bharat Venkitesh",
"Stephen Gou",
"Phil Blunsom",
"Ahmet Üstün",
"Sara Hooker"
] |
Emergent properties have been widely adopted as a term to describe behavior
not present in smaller models but observed in larger models. Recent work
suggests that the trade-off incurred by quantization is also an emergent
property, with sharp drops in performance in models over 6B parameters. In this
work, we ask "are quantization cliffs in performance solely a factor of scale?"
Against a backdrop of increased research focus on why certain emergent
properties surface at scale, this work provides a useful counter-example. We
posit that it is possible to optimize for a quantization friendly training
recipe that suppresses large activation magnitude outliers. Here, we find that
outlier dimensions are not an inherent product of scale, but rather sensitive
to the optimization conditions present during pre-training. This both opens up
directions for more efficient quantization, and poses the question of whether
other emergent properties are inherent or can be altered and conditioned by
optimization and architecture design choices. We successfully quantize models
ranging in size from 410M to 52B with minimal degradation in performance.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19349
|
2023-05-30T18:22:09Z
|
On Riemannian Projection-free Online Learning
|
[
"Zihao Hu",
"Guanghui Wang",
"Jacob Abernethy"
] |
The projection operation is a critical component in a wide range of
optimization algorithms, such as online gradient descent (OGD), for enforcing
constraints and achieving optimal regret bounds. However, it suffers from
computational complexity limitations in high-dimensional settings or when
dealing with ill-conditioned constraint sets. Projection-free algorithms
address this issue by replacing the projection oracle with more efficient
optimization subroutines. But to date, these methods have been developed
primarily in the Euclidean setting, and while there has been growing interest
in optimization on Riemannian manifolds, there has been essentially no work in
trying to utilize projection-free tools here. An apparent issue is that
non-trivial affine functions are generally non-convex in such domains. In this
paper, we present methods for obtaining sub-linear regret guarantees in online
geodesically convex optimization on curved spaces for two scenarios: when we
have access to (a) a separation oracle or (b) a linear optimization oracle. For
geodesically convex losses, and when a separation oracle is available, our
algorithms achieve $O(T^{1/2}\:)$ and $O(T^{3/4}\;)$ adaptive regret guarantees
in the full information setting and the bandit setting, respectively. When a
linear optimization oracle is available, we obtain regret rates of
$O(T^{3/4}\;)$ for geodesically convex losses and $O(T^{2/3}\; log T )$ for
strongly geodesically convex losses
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19375
|
2023-05-30T19:31:31Z
|
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance
Prediction
|
[
"Ana Nikolikj",
"Michal Pluháček",
"Carola Doerr",
"Peter Korošec",
"Tome Eftimov"
] |
Leave-one-problem-out (LOPO) performance prediction requires machine learning
(ML) models to extrapolate algorithms' performance from a set of training
problems to a previously unseen problem. LOPO is a very challenging task even
for state-of-the-art approaches. Models that work well in the easier
leave-one-instance-out scenario often fail to generalize well to the LOPO
setting. To address the LOPO problem, recent work suggested enriching standard
random forest (RF) performance regression models with a weighted average of
algorithms' performance on training problems that are considered similar to a
test problem. More precisely, in this RF+clust approach, the weights are chosen
proportionally to the distances of the problems in some feature space. Here in
this work, we extend the RF+clust approach by adjusting the distance-based
weights with the importance of the features for performance regression. That
is, instead of considering cosine distance in the feature space, we consider a
weighted distance measure, with weights depending on the relevance of the
feature for the regression model. Our empirical evaluation of the modified
RF+clust approach on the CEC 2014 benchmark suite confirms its advantages over
the naive distance measure. However, we also observe room for improvement, in
particular with respect to more expressive feature portfolios.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19407
|
2023-05-30T20:44:14Z
|
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site
Selection
|
[
"Brandon Theodorou",
"Lucas Glass",
"Cao Xiao",
"Jimeng Sun"
] |
Despite many efforts to address the disparities, the underrepresentation of
gender, racial, and ethnic minorities in clinical trials remains a problem and
undermines the efficacy of treatments on minorities. This paper focuses on the
trial site selection task and proposes FRAMM, a deep reinforcement learning
framework for fair trial site selection. We focus on addressing two real-world
challenges that affect fair trial sites selection: the data modalities are
often not complete for many potential trial sites, and the site selection needs
to simultaneously optimize for both enrollment and diversity since the problem
is necessarily a trade-off between the two with the only possible way to
increase diversity post-selection being through limiting enrollment via caps.
To address the missing data challenge, FRAMM has a modality encoder with a
masked cross-attention mechanism for handling missing data, bypassing data
imputation and the need for complete data in training. To handle the need for
making efficient trade-offs, FRAMM uses deep reinforcement learning with a
specifically designed reward function that simultaneously optimizes for both
enrollment and fairness.
We evaluate FRAMM using 4,392 real-world clinical trials ranging from 2016 to
2021 and show that FRAMM outperforms the leading baseline in enrollment-only
settings while also achieving large gains in diversity. Specifically, it is
able to produce a 9% improvement in diversity with similar enrollment levels
over the leading baselines. That improved diversity is further manifested in
achieving up to a 14% increase in Hispanic enrollment, 27% increase in Black
enrollment, and 60% increase in Asian enrollment compared to selecting sites
with an enrollment-only model.
|
[
"cs.AI",
"cs.LG"
] | false |
2305.19416
|
2023-05-30T21:15:45Z
|
KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned
Stochastic Optimization
|
[
"Jonathan Mei",
"Alexander Moreno",
"Luke Walters"
] |
Second order stochastic optimizers allow parameter update step size and
direction to adapt to loss curvature, but have traditionally required too much
memory and compute for deep learning. Recently, Shampoo [Gupta et al., 2018]
introduced a Kronecker factored preconditioner to reduce these requirements: it
is used for large deep models [Anil et al., 2020] and in production [Anil et
al., 2022]. However, it takes inverse matrix roots of ill-conditioned matrices.
This requires 64-bit precision, imposing strong hardware constraints. In this
paper, we propose a novel factorization, Kronecker Approximation-Domination
(KrAD). Using KrAD, we update a matrix that directly approximates the inverse
empirical Fisher matrix (like full matrix AdaGrad), avoiding inversion and
hence 64-bit precision. We then propose KrADagrad$^\star$, with similar
computational costs to Shampoo and the same regret. Synthetic ill-conditioned
experiments show improved performance over Shampoo for 32-bit precision, while
for several real datasets we have comparable or better generalization.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19800
|
2023-05-30T16:39:18Z
|
RINGER: Rapid Conformer Generation for Macrocycles with
Sequence-Conditioned Internal Coordinate Diffusion
|
[
"Colin A. Grambow",
"Hayley Weir",
"Nathaniel L. Diamant",
"Alex M. Tseng",
"Tommaso Biancalani",
"Gabriele Scalia",
"Kangway V. Chuang"
] |
Macrocyclic peptides are an emerging therapeutic modality, yet computational
approaches for accurately sampling their diverse 3D ensembles remain
challenging due to their conformational diversity and geometric constraints.
Here, we introduce RINGER, a diffusion-based transformer model for
sequence-conditioned generation of macrocycle structures based on internal
coordinates. RINGER provides fast backbone sampling while respecting key
structural invariances of cyclic peptides. Through extensive benchmarking and
analysis against gold-standard conformer ensembles of cyclic peptides generated
with metadynamics, we demonstrate how RINGER generates both high-quality and
diverse geometries at a fraction of the computational cost. Our work lays the
foundation for improved sampling of cyclic geometries and the development of
geometric learning methods for peptides.
|
[
"q-bio.BM",
"cs.LG"
] | false |
2306.00012
|
2023-05-30T02:27:17Z
|
Graph Neural Network for spatiotemporal data: methods and applications
|
[
"Yun Li",
"Dazhou Yu",
"Zhenke Liu",
"Minxing Zhang",
"Xiaoyun Gong",
"Liang Zhao"
] |
In the era of big data, there has been a surge in the availability of data
containing rich spatial and temporal information, offering valuable insights
into dynamic systems and processes for applications such as weather
forecasting, natural disaster management, intelligent transport systems, and
precision agriculture. Graph neural networks (GNNs) have emerged as a powerful
tool for modeling and understanding data with dependencies to each other such
as spatial and temporal dependencies. There is a large amount of existing work
that focuses on addressing the complex spatial and temporal dependencies in
spatiotemporal data using GNNs. However, the strong interdisciplinary nature of
spatiotemporal data has created numerous GNNs variants specifically designed
for distinct application domains. Although the techniques are generally
applicable across various domains, cross-referencing these methods remains
essential yet challenging due to the absence of a comprehensive literature
review on GNNs for spatiotemporal data. This article aims to provide a
systematic and comprehensive overview of the technologies and applications of
GNNs in the spatiotemporal domain. First, the ways of constructing graphs from
spatiotemporal data are summarized to help domain experts understand how to
generate graphs from various types of spatiotemporal data. Then, a systematic
categorization and summary of existing spatiotemporal GNNs are presented to
enable domain experts to identify suitable techniques and to support model
developers in advancing their research. Moreover, a comprehensive overview of
significant applications in the spatiotemporal domain is offered to introduce a
broader range of applications to model developers and domain experts, assisting
them in exploring potential research topics and enhancing the impact of their
work. Finally, open challenges and future directions are discussed.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.00015
|
2023-05-30T10:48:59Z
|
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning
Benchmarks
|
[
"Yuwen Li",
"Miao Xiong",
"Bryan Hooi"
] |
Label errors have been found to be prevalent in popular text, vision, and
audio datasets, which heavily influence the safe development and evaluation of
machine learning algorithms. Despite increasing efforts towards improving the
quality of generic data types, such as images and texts, the problem of
mislabel detection in graph data remains underexplored. To bridge the gap, we
explore mislabelling issues in popular real-world graph datasets and propose
GraphCleaner, a post-hoc method to detect and correct these mislabelled nodes
in graph datasets. GraphCleaner combines the novel ideas of 1) Synthetic
Mislabel Dataset Generation, which seeks to generate realistic mislabels; and
2) Neighborhood-Aware Mislabel Detection, where neighborhood dependency is
exploited in both labels and base classifier predictions. Empirical evaluations
on 6 datasets and 6 experimental settings demonstrate that GraphCleaner
outperforms the closest baseline, with an average improvement of 0.14 in F1
score, and 0.16 in MCC. On real-data case studies, GraphCleaner detects real
and previously unknown mislabels in popular graph benchmarks: PubMed, Cora,
CiteSeer and OGB-arxiv; we find that at least 6.91% of PubMed data is
mislabelled or ambiguous, and simply removing these mislabelled data can boost
evaluation performance from 86.71% to 89.11%.
|
[
"cs.LG",
"cs.AI"
] | false |
2306.05285
|
2023-05-30T15:12:59Z
|
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based
Human Activity Recognition
|
[
"Si Zuo",
"Vitor Fortes Rey",
"Sungho Suh",
"Stephan Sigg",
"Paul Lukowicz"
] |
Recognizing human activities from sensor data is a vital task in various
domains, but obtaining diverse and labeled sensor data remains challenging and
costly. In this paper, we propose an unsupervised statistical feature-guided
diffusion model for sensor-based human activity recognition. The proposed
method aims to generate synthetic time-series sensor data without relying on
labeled data, addressing the scarcity and annotation difficulties associated
with real-world sensor data. By conditioning the diffusion model on statistical
information such as mean, standard deviation, Z-score, and skewness, we
generate diverse and representative synthetic sensor data. We conducted
experiments on public human activity recognition datasets and compared the
proposed method to conventional oversampling methods and state-of-the-art
generative adversarial network methods. The experimental results demonstrate
that the proposed method can improve the performance of human activity
recognition and outperform existing techniques.
|
[
"eess.SP",
"cs.LG"
] | false |
2306.05289
|
2023-05-30T10:32:04Z
|
Predictive and diagnosis models of stroke from hemodynamic signal
monitoring
|
[
"Luis García-Terriza",
"José L. Risco-Martín",
"Gemma Reig Roselló",
"José L. Ayala"
] |
This work presents a novel and promising approach to the clinical management
of acute stroke. Using machine learning techniques, our research has succeeded
in developing accurate diagnosis and prediction real-time models from
hemodynamic data. These models are able to diagnose stroke subtype with 30
minutes of monitoring, to predict the exitus during the first 3 hours of
monitoring, and to predict the stroke recurrence in just 15 minutes of
monitoring. Patients with difficult access to a \acrshort{CT} scan, and all
patients that arrive at the stroke unit of a specialized hospital will benefit
from these positive results. The results obtained from the real-time developed
models are the following: stroke diagnosis around $98\%$ precision ($97.8\%$
Sensitivity, $99.5\%$ Specificity), exitus prediction with $99.8\%$ precision
($99.8\%$ Sens., $99.9\%$ Spec.) and $98\%$ precision predicting stroke
recurrence ($98\%$ Sens., $99\%$ Spec.).
|
[
"eess.SP",
"cs.LG"
] | false |
2305.18779
|
2023-05-30T06:24:30Z
|
It begins with a boundary: A geometric view on probabilistically robust
learning
|
[
"Leon Bungert",
"Nicolás García Trillos",
"Matt Jacobs",
"Daniel McKenzie",
"Đorđe Nikolić",
"Qingsong Wang"
] |
Although deep neural networks have achieved super-human performance on many
classification tasks, they often exhibit a worrying lack of robustness towards
adversarially generated examples. Thus, considerable effort has been invested
into reformulating Empirical Risk Minimization (ERM) into an adversarially
robust framework. Recently, attention has shifted towards approaches which
interpolate between the robustness offered by adversarial training and the
higher clean accuracy and faster training times of ERM. In this paper, we take
a fresh and geometric view on one such method -- Probabilistically Robust
Learning (PRL) (Robey et al., ICML, 2022). We propose a geometric framework for
understanding PRL, which allows us to identify a subtle flaw in its original
formulation and to introduce a family of probabilistic nonlocal perimeter
functionals to address this. We prove existence of solutions using novel
relaxation methods and study properties as well as local limits of the
introduced perimeters.
|
[
"cs.LG",
"math.AP",
"math.OC",
"stat.ML"
] | false |
2305.18840
|
2023-05-30T08:33:50Z
|
Learning Perturbations to Explain Time Series Predictions
|
[
"Joseph Enguehard"
] |
Explaining predictions based on multivariate time series data carries the
additional difficulty of handling not only multiple features, but also time
dependencies. It matters not only what happened, but also when, and the same
feature could have a very different impact on a prediction depending on this
time information. Previous work has used perturbation-based saliency methods to
tackle this issue, perturbing an input using a trainable mask to discover which
features at which times are driving the predictions. However these methods
introduce fixed perturbations, inspired from similar methods on static data,
while there seems to be little motivation to do so on temporal data. In this
work, we aim to explain predictions by learning not only masks, but also
associated perturbations. We empirically show that learning these perturbations
significantly improves the quality of these explanations on time series data.
|
[
"cs.LG",
"cs.AI",
"stat.ML"
] | false |
2305.18856
|
2023-05-30T08:50:22Z
|
A Federated Channel Modeling System using Generative Neural Networks
|
[
"Saira Bano",
"Pietro Cassarà",
"Nicola Tonellotto",
"Alberto Gotta"
] |
The paper proposes a data-driven approach to air-to-ground channel estimation
in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike
traditional centralized learning methods that are specific to certain
geographical areas and inappropriate for others, we propose a generalized model
that uses Federated Learning (FL) for channel estimation and can predict the
air-to-ground path loss between a low-altitude platform and a terrestrial
terminal. To this end, our proposed FL-based Generative Adversarial Network
(FL-GAN) is designed to function as a generative data model that can learn
different types of data distributions and generate realistic patterns from the
same distributions without requiring prior data analysis before the training
phase. To evaluate the effectiveness of the proposed model, we evaluate its
performance using Kullback-Leibler divergence (KL), and Wasserstein distance
between the synthetic data distribution generated by the model and the actual
data distribution. We also compare the proposed technique with other generative
models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN
models. The results of the study show that the synthetic data generated by
FL-GAN has the highest similarity in distribution with the real data. This
shows the effectiveness of the proposed approach in generating data-driven
channel models that can be used in different regions
|
[
"cs.NI",
"cs.DC",
"cs.LG",
"C.2.4"
] | false |
2305.18929
|
2023-05-30T10:41:42Z
|
Clip21: Error Feedback for Gradient Clipping
|
[
"Sarit Khirirat",
"Eduard Gorbunov",
"Samuel Horváth",
"Rustem Islamov",
"Fakhri Karray",
"Peter Richtárik"
] |
Motivated by the increasing popularity and importance of large-scale training
under differential privacy (DP) constraints, we study distributed gradient
methods with gradient clipping, i.e., clipping applied to the gradients
computed from local information at the nodes. While gradient clipping is an
essential tool for injecting formal DP guarantees into gradient-based methods
[1], it also induces bias which causes serious convergence issues specific to
the distributed setting. Inspired by recent progress in the error-feedback
literature which is focused on taming the bias/error introduced by
communication compression operators such as Top-$k$ [2], and mathematical
similarities between the clipping operator and contractive compression
operators, we design Clip21 -- the first provably effective and practically
useful error feedback mechanism for distributed methods with gradient clipping.
We prove that our method converges at the same
$\mathcal{O}\left(\frac{1}{K}\right)$ rate as distributed gradient descent in
the smooth nonconvex regime, which improves the previous best
$\mathcal{O}\left(\frac{1}{\sqrt{K}}\right)$ rate which was obtained under
significantly stronger assumptions. Our method converges significantly faster
in practice than competing methods.
|
[
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.18951
|
2023-05-30T11:34:57Z
|
Subequivariant Graph Reinforcement Learning in 3D Environments
|
[
"Runfa Chen",
"Jiaqi Han",
"Fuchun Sun",
"Wenbing Huang"
] |
Learning a shared policy that guides the locomotion of different agents is of
core interest in Reinforcement Learning (RL), which leads to the study of
morphology-agnostic RL. However, existing benchmarks are highly restrictive in
the choice of starting point and target point, constraining the movement of the
agents within 2D space. In this work, we propose a novel setup for
morphology-agnostic RL, dubbed Subequivariant Graph RL in 3D environments
(3D-SGRL). Specifically, we first introduce a new set of more practical yet
challenging benchmarks in 3D space that allows the agent to have full
Degree-of-Freedoms to explore in arbitrary directions starting from arbitrary
configurations. Moreover, to optimize the policy over the enlarged state-action
space, we propose to inject geometric symmetry, i.e., subequivariance, into the
modeling of the policy and Q-function such that the policy can generalize to
all directions, improving exploration efficiency. This goal is achieved by a
novel SubEquivariant Transformer (SET) that permits expressive message
exchange. Finally, we evaluate the proposed method on the proposed benchmarks,
where our method consistently and significantly outperforms existing approaches
on single-task, multi-task, and zero-shot generalization scenarios. Extensive
ablations are also conducted to verify our design. Code and videos are
available on our project page: https://alpc91.github.io/SGRL/.
|
[
"cs.LG",
"cs.AI",
"cs.RO"
] | false |
2305.18965
|
2023-05-30T11:53:40Z
|
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
|
[
"Qiyu Kang",
"Kai Zhao",
"Yang Song",
"Sijie Wang",
"Wee Peng Tay"
] |
In the graph node embedding problem, embedding spaces can vary significantly
for different data types, leading to the need for different GNN model types. In
this paper, we model the embedding update of a node feature as a Hamiltonian
orbit over time. Since the Hamiltonian orbits generalize the exponential maps,
this approach allows us to learn the underlying manifold of the graph in
training, in contrast to most of the existing literature that assumes a fixed
graph embedding manifold with a closed exponential map solution. Our proposed
node embedding strategy can automatically learn, without extensive tuning, the
underlying geometry of any given graph dataset even if it has diverse
geometries. We test Hamiltonian functions of different forms and verify the
performance of our approach on two graph node embedding downstream tasks: node
classification and link prediction. Numerical experiments demonstrate that our
approach adapts better to different types of graph datasets than popular
state-of-the-art graph node embedding GNNs. The code is available at
\url{https://github.com/zknus/Hamiltonian-GNN}.
|
[
"cs.LG",
"math.DS",
"physics.class-ph"
] | false |
2305.19043
|
2023-05-30T13:58:50Z
|
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality
Reduction
|
[
"Guillaume Huguet",
"Alexander Tong",
"Edward De Brouwer",
"Yanlei Zhang",
"Guy Wolf",
"Ian Adelstein",
"Smita Krishnaswamy"
] |
Diffusion-based manifold learning methods have proven useful in
representation learning and dimensionality reduction of modern high
dimensional, high throughput, noisy datasets. Such datasets are especially
present in fields like biology and physics. While it is thought that these
methods preserve underlying manifold structure of data by learning a proxy for
geodesic distances, no specific theoretical links have been established. Here,
we establish such a link via results in Riemannian geometry explicitly
connecting heat diffusion to manifold distances. In this process, we also
formulate a more general heat kernel based manifold embedding method that we
call heat geodesic embeddings. This novel perspective makes clearer the choices
available in manifold learning and denoising. Results show that our method
outperforms existing state of the art in preserving ground truth manifold
distances, and preserving cluster structure in toy datasets. We also showcase
our method on single cell RNA-sequencing datasets with both continuum and
cluster structure, where our method enables interpolation of withheld
timepoints of data. Finally, we show that parameters of our more general method
can be configured to give results similar to PHATE (a state-of-the-art
diffusion based manifold learning method) as well as SNE (an
attraction/repulsion neighborhood based method that forms the basis of t-SNE).
|
[
"cs.LG",
"q-bio.GN",
"q-bio.QM",
"stat.ML"
] | false |
2305.19059
|
2023-05-30T14:20:51Z
|
Rank-adaptive spectral pruning of convolutional layers during training
|
[
"Emanuele Zangrando",
"Steffen Schotthöfer",
"Gianluca Ceruti",
"Jonas Kusch",
"Francesco Tudisco"
] |
The computing cost and memory demand of deep learning pipelines have grown
fast in recent years and thus a variety of pruning techniques have been
developed to reduce model parameters. The majority of these techniques focus on
reducing inference costs by pruning the network after a pass of full training.
A smaller number of methods address the reduction of training costs, mostly
based on compressing the network via low-rank layer factorizations. Despite
their efficiency for linear layers, these methods fail to effectively handle
convolutional filters. In this work, we propose a low-parametric training
method that factorizes the convolutions into tensor Tucker format and
adaptively prunes the Tucker ranks of the convolutional kernel during training.
Leveraging fundamental results from geometric integration theory of
differential equations on tensor manifolds, we obtain a robust training
algorithm that provably approximates the full baseline performance and
guarantees loss descent. A variety of experiments against the full model and
alternative low-rank baselines are implemented, showing that the proposed
method drastically reduces the training costs, while achieving high
performance, comparable to or better than the full baseline, and consistently
outperforms competing low-rank approaches.
|
[
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | false |
2305.19077
|
2023-05-30T14:40:40Z
|
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep
Hierarchical Reinforcement Learning in SDN
|
[
"Miao Ye",
"Chenwei Zhao",
"Xingsi Xue",
"Jinqiang Li",
"Hongwen Hu",
"Yejin Yang",
"Qiuxiang Jiang"
] |
The optimal multicast tree problem in the Software-Defined Networking (SDN)
multicast routing is an NP-hard combinatorial optimization problem. Although
existing SDN intelligent solution methods, which are based on deep
reinforcement learning, can dynamically adapt to complex network link state
changes, these methods are plagued by problems such as redundant branches,
large action space, and slow agent convergence. In this paper, an SDN
intelligent multicast routing algorithm based on deep hierarchical
reinforcement learning is proposed to circumvent the aforementioned problems.
First, the multicast tree construction problem is decomposed into two
sub-problems: the fork node selection problem and the construction of the
optimal path from the fork node to the destination node. Second, based on the
information characteristics of SDN global network perception, the multicast
tree state matrix, link bandwidth matrix, link delay matrix, link packet loss
rate matrix, and sub-goal matrix are designed as the state space of intrinsic
and meta controllers. Then, in order to mitigate the excessive action space,
our approach constructs different action spaces at the upper and lower levels.
The meta-controller generates an action space using network nodes to select the
fork node, and the intrinsic controller uses the adjacent edges of the current
node as its action space, thus implementing four different action selection
strategies in the construction of the multicast tree. To facilitate the
intelligent agent in constructing the optimal multicast tree with greater
speed, we developed alternative reward strategies that distinguish between
single-step node actions and multi-step actions towards multiple destination
nodes.
|
[
"cs.AI",
"cs.LG",
"cs.NI"
] | false |
2305.19123
|
2023-05-30T15:31:44Z
|
ELSA: Efficient Label Shift Adaptation through the Lens of
Semiparametric Models
|
[
"Qinglong Tian",
"Xin Zhang",
"Jiwei Zhao"
] |
We study the domain adaptation problem with label shift in this work. Under
the label shift context, the marginal distribution of the label varies across
the training and testing datasets, while the conditional distribution of
features given the label is the same. Traditional label shift adaptation
methods either suffer from large estimation errors or require cumbersome
post-prediction calibrations. To address these issues, we first propose a
moment-matching framework for adapting the label shift based on the geometry of
the influence function. Under such a framework, we propose a novel method named
\underline{E}fficient \underline{L}abel \underline{S}hift
\underline{A}daptation (ELSA), in which the adaptation weights can be estimated
by solving linear systems. Theoretically, the ELSA estimator is
$\sqrt{n}$-consistent ($n$ is the sample size of the source data) and
asymptotically normal. Empirically, we show that ELSA can achieve
state-of-the-art estimation performances without post-prediction calibrations,
thus, gaining computational efficiency.
|
[
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | false |
2305.19167
|
2023-05-30T16:14:16Z
|
Reduced Precision Floating-Point Optimization for Deep Neural Network
On-Device Learning on MicroControllers
|
[
"Davide Nadalini",
"Manuele Rusci",
"Luca Benini",
"Francesco Conti"
] |
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units
(MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep
Neural Network (DNN) models in future TinyML applications. This paper tackles
this challenge by introducing a novel reduced precision optimization technique
for ODL primitives on MCU-class devices, leveraging the State-of-Art
advancements in RISC-V RV32 architectures with support for vectorized 16-bit
floating-point (FP16) Single-Instruction Multiple-Data (SIMD) operations. Our
approach for the Forward and Backward steps of the Back-Propagation training
algorithm is composed of specialized shape transform operators and Matrix
Multiplication (MM) kernels, accelerated with parallelization and loop
unrolling. When evaluated on a single training step of a 2D Convolution layer,
the SIMD-optimized FP16 primitives result up to 1.72$\times$ faster than the
FP32 baseline on a RISC-V-based 8+1-core MCU. An average computing efficiency
of 3.11 Multiply and Accumulate operations per clock cycle (MAC/clk) and 0.81
MAC/clk is measured for the end-to-end training tasks of a ResNet8 and a DS-CNN
for Image Classification and Keyword Spotting, respectively -- requiring 17.1
ms and 6.4 ms on the target platform to compute a training step on a single
sample. Overall, our approach results more than two orders of magnitude faster
than existing ODL software frameworks for single-core MCUs and outperforms by
1.6 $\times$ previous FP32 parallel implementations on a Continual Learning
setup.
|
[
"cs.LG",
"cs.AI",
"cs.DC"
] | false |
2305.19184
|
2023-05-30T16:29:33Z
|
Leveraging Semantic Information for Efficient Self-Supervised Emotion
Recognition with Audio-Textual Distilled Models
|
[
"Danilo de Oliveira",
"Navin Raj Prabhu",
"Timo Gerkmann"
] |
In large part due to their implicit semantic modeling, self-supervised
learning (SSL) methods have significantly increased the performance of valence
recognition in speech emotion recognition (SER) systems. Yet, their large size
may often hinder practical implementations. In this work, we take HuBERT as an
example of an SSL model and analyze the relevance of each of its layers for
SER. We show that shallow layers are more important for arousal recognition
while deeper layers are more important for valence. This observation motivates
the importance of additional textual information for accurate valence
recognition, as the distilled framework lacks the depth of its large-scale SSL
teacher. Thus, we propose an audio-textual distilled SSL framework that, while
having only ~20% of the trainable parameters of a large SSL model, achieves on
par performance across the three emotion dimensions (arousal, valence,
dominance) on the MSP-Podcast v1.10 dataset.
|
[
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2305.19214
|
2023-05-30T17:03:36Z
|
Design and implementation of intelligent packet filtering in IoT
microcontroller-based devices
|
[
"Gustavo de Carvalho Bertoli",
"Gabriel Victor C. Fernandes",
"Pedro H. Borges Monici",
"César H. de Araujo Guibo",
"Lourenço Alves Pereira Jr.",
"Aldri Santos"
] |
Internet of Things (IoT) devices are increasingly pervasive and essential
components in enabling new applications and services. However, their widespread
use also exposes them to exploitable vulnerabilities and flaws that can lead to
significant losses. In this context, ensuring robust cybersecurity measures is
essential to protect IoT devices from malicious attacks. However, the current
solutions that provide flexible policy specifications and higher security
levels for IoT devices are scarce. To address this gap, we introduce T800, a
low-resource packet filter that utilizes machine learning (ML) algorithms to
classify packets in IoT devices. We present a detailed performance benchmarking
framework and demonstrate T800's effectiveness on the ESP32 system-on-chip
microcontroller and ESP-IDF framework. Our evaluation shows that T800 is an
efficient solution that increases device computational capacity by excluding
unsolicited malicious traffic from the processing pipeline. Additionally, T800
is adaptable to different systems and provides a well-documented performance
evaluation strategy for security ML-based mechanisms on ESP32-based IoT
systems. Our research contributes to improving the cybersecurity of
resource-constrained IoT devices and provides a scalable, efficient solution
that can be used to enhance the security of IoT systems.
|
[
"cs.CR",
"cs.LG",
"cs.NI"
] | false |
2305.19267
|
2023-05-30T17:57:34Z
|
Parallelized Acquisition for Active Learning using Monte Carlo Sampling
|
[
"Jesús Torrado",
"Nils Schöneberg",
"Jonas El Gammal"
] |
Bayesian inference remains one of the most important tool-kits for any
scientist, but increasingly expensive likelihood functions are required for
ever-more complex experiments, raising the cost of generating a Monte Carlo
sample of the posterior. Recent attention has been directed towards the use of
emulators of the posterior based on Gaussian Process (GP) regression combined
with active sampling to achieve comparable precision with far fewer costly
likelihood evaluations. Key to this approach is the batched acquisition of
proposals, so that the true posterior can be evaluated in parallel. This is
usually achieved via sequential maximization of the highly multimodal
acquisition function. Unfortunately, this approach parallelizes poorly and is
prone to getting stuck in local maxima. Our approach addresses this issue by
generating nearly-optimal batches of candidates using an almost-embarrassingly
parallel Nested Sampler on the mean prediction of the GP. The resulting
nearly-sorted Monte Carlo sample is used to generate a batch of candidates
ranked according to their sequentially conditioned acquisition function values
at little cost. The final sample can also be used for inferring marginal
quantities. Our proposed implementation (NORA) demonstrates comparable accuracy
to sequential conditioned acquisition optimization and efficient
parallelization in various synthetic and cosmological inference problems.
|
[
"stat.ML",
"astro-ph.CO",
"astro-ph.IM",
"cs.LG"
] | false |
2305.19304
|
2023-05-30T15:42:13Z
|
Audio classification using ML methods
|
[
"Krishna Kumar"
] |
Machine Learning systems have achieved outstanding performance in different
domains. In this paper machine learning methods have been applied to
classification task to classify music genre. The code shows how to extract
features from audio files and classify them using supervised learning into 2
genres namely classical and metal. Algorithms used are LogisticRegression, SVC
using different kernals (linear, sigmoid, rbf and poly), KNeighborsClassifier ,
RandomForestClassifier, DecisionTreeClassifier and GaussianNB.
|
[
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2305.19350
|
2023-05-30T18:25:11Z
|
Non-convex Bayesian Learning via Stochastic Gradient Markov Chain Monte
Carlo
|
[
"Wei Deng"
] |
The rise of artificial intelligence (AI) hinges on the efficient training of
modern deep neural networks (DNNs) for non-convex optimization and uncertainty
quantification, which boils down to a non-convex Bayesian learning problem. A
standard tool to handle the problem is Langevin Monte Carlo, which proposes to
approximate the posterior distribution with theoretical guarantees. In this
thesis, we start with the replica exchange Langevin Monte Carlo (also known as
parallel tempering), which proposes appropriate swaps between exploration and
exploitation to achieve accelerations. However, the na\"ive extension of swaps
to big data problems leads to a large bias, and bias-corrected swaps are
required. Such a mechanism leads to few effective swaps and insignificant
accelerations. To alleviate this issue, we first propose a control variates
method to reduce the variance of noisy energy estimators and show a potential
to accelerate the exponential convergence. We also present the population-chain
replica exchange based on non-reversibility and obtain an optimal round-trip
rate for deep learning. In the second part of the thesis, we study scalable
dynamic importance sampling algorithms based on stochastic approximation.
Traditional dynamic importance sampling algorithms have achieved success,
however, the lack of scalability has greatly limited their extensions to big
data. To handle this scalability issue, we resolve the vanishing gradient
problem and propose two dynamic importance sampling algorithms. Theoretically,
we establish the stability condition for the underlying ordinary differential
equation (ODE) system and guarantee the asymptotic convergence of the latent
variable to the desired fixed point. Interestingly, such a result still holds
given non-convex energy landscapes.
|
[
"stat.CO",
"cs.LG",
"math.PR",
"stat.ML"
] | false |
2305.19354
|
2023-05-30T18:31:44Z
|
Uncovering multifunctional mechano-intelligence in and through phononic
metastructures harnessing physical reservoir computing
|
[
"Yuning Zhang",
"Aditya Deshmukh",
"K. W. Wang"
] |
The recent advances in autonomous systems have prompted a strong demand for
the next generation of adaptive structures and materials to possess more
built-in intelligence in their mechanical domain, the so-called
mechano-intelligence (MI). Previous MI attempts mainly focused on specific
designs and case studies to realize limited aspects of MI, and there is a lack
of a systematic foundation in constructing and integrating the different
elements of intelligence in an effective and efficient manner. Here, we propose
a new approach to create the needed foundation in realizing integrated
multifunctional MI via a physical reservoir computing (PRC) framework. That is,
to concurrently embody computing power and the various elements of
intelligence, namely perception, decision-making, and commanding, directly in
the mechanical domain, advancing from conventional adaptive structures that
rely solely on add-on digital computers and massive electronics to achieve
intelligence. As an exemplar platform, we construct a mechanically intelligent
phononic metastructure with the integrated elements of MI by harnessing the PRC
power hidden in their high-degree-of-freedom nonlinear dynamics. Through
analyses and experimental investigations, we uncover multiple adaptive
structural functions ranging from self-tuning wave controls to wave-based logic
gates. This research will provide the basis for creating future new structures
that would greatly surpass the state of the art - such as lower power
consumption, more direct interactions, and much better survivability in harsh
environment or under cyberattacks. Moreover, it will enable the addition of new
functions and autonomy to systems without overburdening the onboard computers.
|
[
"physics.app-ph",
"cs.ET",
"cs.LG"
] | false |
2305.19421
|
2023-05-30T21:27:05Z
|
Data and Knowledge for Overtaking Scenarios in Autonomous Driving
|
[
"Mariana Pinto",
"Inês Dutra",
"Joaquim Fonseca"
] |
Autonomous driving has become one of the most popular research topics within
Artificial Intelligence. An autonomous vehicle is understood as a system that
combines perception, decision-making, planning, and control. All of those tasks
require that the vehicle collects surrounding data in order to make a good
decision and action. In particular, the overtaking maneuver is one of the most
critical actions of driving. The process involves lane changes, acceleration
and deceleration actions, and estimation of the speed and distance of the
vehicle in front or in the lane in which it is moving. Despite the amount of
work available in the literature, just a few handle overtaking maneuvers and,
because overtaking can be risky, no real-world dataset is available. This work
contributes in this area by presenting a new synthetic dataset whose focus is
the overtaking maneuver. We start by performing a thorough review of the state
of the art in autonomous driving and then explore the main datasets found in
the literature (public and private, synthetic and real), highlighting their
limitations, and suggesting a new set of features whose focus is the overtaking
maneuver.
|
[
"cs.RO",
"cs.AI",
"cs.LG"
] | false |
2305.19440
|
2023-05-30T22:22:24Z
|
Machine learning with tree tensor networks, CP rank constraints, and
tensor dropout
|
[
"Hao Chen",
"Thomas Barthel"
] |
Tensor networks approximate order-$N$ tensors with a reduced number of
degrees of freedom that is only polynomial in $N$ and arranged as a network of
partially contracted smaller tensors. As suggested in [arXiv:2205.15296] in the
context of quantum many-body physics, computation costs can be further
substantially reduced by imposing constraints on the canonical polyadic (CP)
rank of the tensors in such networks. Here we demonstrate how tree tensor
networks (TTN) with CP rank constraints and tensor dropout can be used in
machine learning. The approach is found to outperform other tensor-network
based methods in Fashion-MNIST image classification. A low-rank TTN classifier
with branching ratio $b=4$ reaches test set accuracy 90.3\% with low
computation costs. Consisting of mostly linear elements, tensor network
classifiers avoid the vanishing gradient problem of deep neural networks. The
CP rank constraints have additional advantages: The number of parameters can be
decreased and tuned more freely to control overfitting, improve generalization
properties, and reduce computation costs. They allow us to employ trees with
large branching ratios which substantially improves the representation power.
|
[
"cs.LG",
"cond-mat.str-el",
"stat.ML"
] | false |
2305.19801
|
2023-05-30T14:48:06Z
|
Predicting protein stability changes under multiple amino acid
substitutions using equivariant graph neural networks
|
[
"Sebastien Boyer",
"Sam Money-Kyrle",
"Oliver Bent"
] |
The accurate prediction of changes in protein stability under multiple amino
acid substitutions is essential for realising true in-silico protein re-design.
To this purpose, we propose improvements to state-of-the-art Deep learning (DL)
protein stability prediction models, enabling first-of-a-kind predictions for
variable numbers of amino acid substitutions, on structural representations, by
decoupling the atomic and residue scales of protein representations. This was
achieved using E(3)-equivariant graph neural networks (EGNNs) for both atomic
environment (AE) embedding and residue-level scoring tasks. Our AE embedder was
used to featurise a residue-level graph, then trained to score mutant stability
($\Delta\Delta G$). To achieve effective training of this predictive EGNN we
have leveraged the unprecedented scale of a new high-throughput protein
stability experimental data-set, Mega-scale. Finally, we demonstrate the
immediately promising results of this procedure, discuss the current
shortcomings, and highlight potential future strategies.
|
[
"q-bio.BM",
"cs.AI",
"cs.LG"
] | false |
2306.00016
|
2023-05-30T12:53:55Z
|
Incorporating Domain Knowledge in Deep Neural Networks for Discrete
Choice Models
|
[
"Shadi Haj-Yahia",
"Omar Mansour",
"Tomer Toledo"
] |
Discrete choice models (DCM) are widely employed in travel demand analysis as
a powerful theoretical econometric framework for understanding and predicting
choice behaviors. DCMs are formed as random utility models (RUM), with their
key advantage of interpretability. However, a core requirement for the
estimation of these models is a priori specification of the associated utility
functions, making them sensitive to modelers' subjective beliefs. Recently,
machine learning (ML) approaches have emerged as a promising avenue for
learning unobserved non-linear relationships in DCMs. However, ML models are
considered "black box" and may not correspond with expected relationships. This
paper proposes a framework that expands the potential of data-driven approaches
for DCM by supporting the development of interpretable models that incorporate
domain knowledge and prior beliefs through constraints. The proposed framework
includes pseudo data samples that represent required relationships and a loss
function that measures their fulfillment, along with observed data, for model
training. The developed framework aims to improve model interpretability by
combining ML's specification flexibility with econometrics and interpretable
behavioral analysis. A case study demonstrates the potential of this framework
for discrete choice analysis.
|
[
"cs.LG",
"cs.AI",
"econ.EM"
] | false |
2306.00023
|
2023-05-30T21:15:21Z
|
Predicting Heart Disease and Reducing Survey Time Using Machine Learning
Algorithms
|
[
"Salahaldeen Rababa",
"Asma Yamin",
"Shuxia Lu",
"Ashraf Obaidat"
] |
Currently, many researchers and analysts are working toward medical diagnosis
enhancement for various diseases. Heart disease is one of the common diseases
that can be considered a significant cause of mortality worldwide. Early
detection of heart disease significantly helps in reducing the risk of heart
failure. Consequently, the Centers for Disease Control and Prevention (CDC)
conducts a health-related telephone survey yearly from over 400,000
participants. However, several concerns arise regarding the reliability of the
data in predicting heart disease and whether all of the survey questions are
strongly related. This study aims to utilize several machine learning
techniques, such as support vector machines and logistic regression, to
investigate the accuracy of the CDC's heart disease survey in the United
States. Furthermore, we use various feature selection methods to identify the
most relevant subset of questions that can be utilized to forecast heart
conditions. To reach a robust conclusion, we perform stability analysis by
randomly sampling the data 300 times. The experimental results show that the
survey data can be useful up to 80% in terms of predicting heart disease, which
significantly improves the diagnostic process before bloodwork and tests. In
addition, the amount of time spent conducting the survey can be reduced by 77%
while maintaining the same level of performance.
|
[
"cs.LG",
"cs.AI",
"stat.AP"
] | false |
2305.18737
|
2023-05-30T04:21:27Z
|
Phase Correction using Deep Learning for Satellite-to-Ground CV-QKD
|
[
"Nathan K. Long",
"Robert Malaney",
"Kenneth J. Grant"
] |
Coherent measurement of quantum signals used for continuous-variable (CV)
quantum key distribution (QKD) across satellite-to-ground channels requires
compensation of phase wavefront distortions caused by atmospheric turbulence.
One compensation technique involves multiplexing classical reference pulses
(RPs) and the quantum signal, with direct phase measurements on the RPs then
used to modulate a real local oscillator (RLO) on the ground - a solution that
also removes some known attacks on CV-QKD. However, this is a cumbersome task
in practice - requiring substantial complexity in equipment requirements and
deployment. As an alternative to this traditional practice, here we introduce a
new method for estimating phase corrections for an RLO by using only intensity
measurements from RPs as input to a convolutional neural network, mitigating
completely the necessity to measure phase wavefronts directly. Conventional
wisdom dictates such an approach would likely be fruitless. However, we show
that the phase correction accuracy needed to provide for non-zero secure key
rates through satellite-to-ground channels is achieved by our intensity-only
measurements. Our work shows, for the first time, how artificial intelligence
algorithms can replace phase-measuring equipment in the context of CV-QKD
delivered from space, thereby delivering an alternate deployment paradigm for
this global quantum-communication application.
|
[
"quant-ph",
"cs.AI",
"cs.CR",
"cs.LG",
"eess.SP"
] | false |
2305.18784
|
2023-05-30T06:35:49Z
|
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
|
[
"Ronshee Chawla",
"Daniel Vial",
"Sanjay Shakkottai",
"R. Srikant"
] |
The study of collaborative multi-agent bandits has attracted significant
attention recently. In light of this, we initiate the study of a new
collaborative setting, consisting of $N$ agents such that each agent is
learning one of $M$ stochastic multi-armed bandits to minimize their group
cumulative regret. We develop decentralized algorithms which facilitate
collaboration between the agents under two scenarios. We characterize the
performance of these algorithms by deriving the per agent cumulative regret and
group regret upper bounds. We also prove lower bounds for the group regret in
this setting, which demonstrates the near-optimal behavior of the proposed
algorithms.
|
[
"cs.LG",
"cs.DC",
"cs.MA",
"cs.SI",
"stat.ML"
] | false |
2306.05358
|
2023-05-30T00:57:51Z
|
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced
Driver-Assistance System
|
[
"Jiwei Guan",
"Lei Pan",
"Chen Wang",
"Shui Yu",
"Longxiang Gao",
"Xi Zheng"
] |
There are increasing concerns about malicious attacks on autonomous vehicles.
In particular, inaudible voice command attacks pose a significant threat as
voice commands become available in autonomous driving systems. How to
empirically defend against these inaudible attacks remains an open question.
Previous research investigates utilizing deep learning-based multimodal fusion
for defense, without considering the model uncertainty in trustworthiness. As
deep learning has been applied to increasingly sensitive tasks, uncertainty
measurement is crucial in helping improve model robustness, especially in
mission-critical scenarios. In this paper, we propose the Multimodal Fusion
Framework (MFF) as an intelligent security system to defend against inaudible
voice command attacks. MFF fuses heterogeneous audio-vision modalities using
VGG family neural networks and achieves the detection accuracy of 92.25% in the
comparative fusion method empirical study. Additionally, extensive experiments
on audio-vision tasks reveal the model's uncertainty. Using Expected
Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to
estimate the predictive distribution for the proposed models. Our findings show
empirically to train robust multimodal models, improve standard accuracy and
provide a further step toward interpretability. Finally, we discuss the pros
and cons of our approach and its applicability for Advanced Driver Assistance
Systems.
|
[
"cs.CR",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2305.19001
|
2023-05-30T12:58:39Z
|
Sharp high-probability sample complexities for policy evaluation with
linear function approximation
|
[
"Gen Li",
"Weichen Wu",
"Yuejie Chi",
"Cong Ma",
"Alessandro Rinaldo",
"Yuting Wei"
] |
This paper is concerned with the problem of policy evaluation with linear
function approximation in discounted infinite horizon Markov decision
processes. We investigate the sample complexities required to guarantee a
predefined estimation error of the best linear coefficients for two widely-used
policy evaluation algorithms: the temporal difference (TD) learning algorithm
and the two-timescale linear TD with gradient correction (TDC) algorithm. In
both the on-policy setting, where observations are generated from the target
policy, and the off-policy setting, where samples are drawn from a behavior
policy potentially different from the target policy, we establish the first
sample complexity bound with high-probability convergence guarantee that
attains the optimal dependence on the tolerance level. We also exhihit an
explicit dependence on problem-related quantities, and show in the on-policy
setting that our upper bound matches the minimax lower bound on crucial problem
parameters, including the choice of the feature maps and the problem dimension.
|
[
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT",
"math.OC",
"math.ST",
"stat.TH"
] | false |
2305.19486
|
2023-05-31T01:46:14Z
|
Noisy-label Learning with Sample Selection based on Noise Rate Estimate
|
[
"Arpit Garg",
"Cuong Nguyen",
"Rafael Felix",
"Thanh-Toan Do",
"Gustavo Carneiro"
] |
Noisy-labels are challenging for deep learning due to the high capacity of
the deep models that can overfit noisy-label training samples. Arguably the
most realistic and coincidentally challenging type of label noise is the
instance-dependent noise (IDN), where the labelling errors are caused by the
ambivalent information present in the images. The most successful label noise
learning techniques to address IDN problems usually contain a noisy-label
sample selection stage to separate clean and noisy-label samples during
training. Such sample selection depends on a criterion, such as loss or
gradient, and on a curriculum to define the proportion of training samples to
be classified as clean at each training epoch.
Even though the estimated noise rate from the training set appears to be a
natural signal to be used in the definition of this curriculum, previous
approaches generally rely on arbitrary thresholds or pre-defined selection
functions to the best of our knowledge. This paper addresses this research gap
by proposing a new noisy-label learning graphical model that can easily
accommodate state-of-the-art (SOTA) noisy-label learning methods and provide
them with a reliable noise rate estimate to be used in a new sample selection
curriculum. We show empirically that our model integrated with many SOTA
methods can improve their results in many IDN benchmarks, including synthetic
and real-world datasets.
|
[
"cs.CV"
] | false |
2305.19513
|
2023-05-31T02:52:38Z
|
Towards Accurate and Reliable Change Detection of Remote Sensing Images
via Knowledge Review and Online Uncertainty Estimation
|
[
"Zhenglai Li",
"Chang Tang",
"Xianju Li",
"Weiying Xie",
"Kun Sun",
"Xinzhong Zhu"
] |
Change detection (CD) is an essential task for various real-world
applications, such as urban management and disaster assessment. However,
previous methods primarily focus on improving the accuracy of CD, while
neglecting the reliability of detection results. In this paper, we propose a
novel change detection network, called AR-CDNet, which is able to provide
accurate change maps and generate pixel-wise uncertainty. Specifically, an
online uncertainty estimation branch is constructed to model the pixel-wise
uncertainty, which is supervised by the difference between predicted change
maps and corresponding ground truth during the training process. Furthermore,
we introduce a knowledge review strategy to distill temporal change knowledge
from low-level features to high-level ones, thereby enhancing the
discriminability of temporal difference features. Finally, we aggregate the
uncertainty-aware features extracted from the online uncertainty estimation
branch with multi-level temporal difference features to improve the accuracy of
CD. Once trained, our AR-CDNet can provide accurate change maps and evaluate
pixel-wise uncertainty without ground truth. Experimental results on two
benchmark datasets demonstrate the superior performance of AR-CDNet in the CD
task. The demo code for our work will be publicly available at
\url{https://github.com/guanyuezhen/AR-CDNet}.
|
[
"cs.CV"
] | false |
2305.19543
|
2023-05-31T04:18:30Z
|
Improving Handwritten OCR with Training Samples Generated by Glyph
Conditional Denoising Diffusion Probabilistic Model
|
[
"Haisong Ding",
"Bozhi Luan",
"Dongnan Gui",
"Kai Chen",
"Qiang Huo"
] |
Constructing a highly accurate handwritten OCR system requires large amounts
of representative training data, which is both time-consuming and expensive to
collect. To mitigate the issue, we propose a denoising diffusion probabilistic
model (DDPM) to generate training samples. This model conditions on a printed
glyph image and creates mappings between printed characters and handwritten
images, thus enabling the generation of photo-realistic handwritten samples
with diverse styles and unseen text contents. However, the text contents in
synthetic images are not always consistent with the glyph conditional images,
leading to unreliable labels of synthetic samples. To address this issue, we
further propose a progressive data filtering strategy to add those samples with
a high confidence of correctness to the training set. Experimental results on
IAM benchmark task show that OCR model trained with augmented DDPM-synthesized
training samples can achieve about 45% relative word error rate reduction
compared with the one trained on real data only.
|
[
"cs.CV"
] | false |
2305.19547
|
2023-05-31T04:27:47Z
|
Inferring and Leveraging Parts from Object Shape for Improving Semantic
Image Synthesis
|
[
"Yuxiang Wei",
"Zhilong Ji",
"Xiaohe Wu",
"Jinfeng Bai",
"Lei Zhang",
"Wangmeng Zuo"
] |
Despite the progress in semantic image synthesis, it remains a challenging
problem to generate photo-realistic parts from input semantic map. Integrating
part segmentation map can undoubtedly benefit image synthesis, but is
bothersome and inconvenient to be provided by users. To improve part synthesis,
this paper presents to infer Parts from Object ShapE (iPOSE) and leverage it
for improving semantic image synthesis. However, albeit several part
segmentation datasets are available, part annotations are still not provided
for many object categories in semantic image synthesis. To circumvent it, we
resort to few-shot regime to learn a PartNet for predicting the object part map
with the guidance of pre-defined support part maps. PartNet can be readily
generalized to handle a new object category when a small number (e.g., 3) of
support part maps for this category are provided. Furthermore, part semantic
modulation is presented to incorporate both inferred part map and semantic map
for image synthesis. Experiments show that our iPOSE not only generates objects
with rich part details, but also enables to control the image synthesis
flexibly. And our iPOSE performs favorably against the state-of-the-art methods
in terms of quantitative and qualitative evaluation. Our code will be publicly
available at https://github.com/csyxwei/iPOSE.
|
[
"cs.CV"
] | false |
2305.19624
|
2023-05-31T07:50:38Z
|
A Multi-Modal Transformer Network for Action Detection
|
[
"Matthew Korban",
"Scott T. Acton",
"Peter Youngs"
] |
This paper proposes a novel multi-modal transformer network for detecting
actions in untrimmed videos. To enrich the action features, our transformer
network utilizes a new multi-modal attention mechanism that computes the
correlations between different spatial and motion modalities combinations.
Exploring such correlations for actions has not been attempted previously. To
use the motion and spatial modality more effectively, we suggest an algorithm
that corrects the motion distortion caused by camera movement. Such motion
distortion, common in untrimmed videos, severely reduces the expressive power
of motion features such as optical flow fields. Our proposed algorithm
outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and
ActivityNet. We also conducted comparative experiments on our new instructional
activity dataset, including a large set of challenging classroom videos
captured from elementary schools.
|
[
"cs.CV"
] | false |
2305.19688
|
2023-05-31T09:31:54Z
|
VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning
Challenges
|
[
"Robert-Jan Bruintjes",
"Attila Lengyel",
"Marcos Baptista Rios",
"Osman Semih Kayhan",
"Davide Zambrano",
"Nergis Tomen",
"Jan van Gemert"
] |
The third edition of the "VIPriors: Visual Inductive Priors for
Data-Efficient Deep Learning" workshop featured four data-impaired challenges,
focusing on addressing the limitations of data availability in training deep
learning models for computer vision tasks. The challenges comprised of four
distinct data-impaired tasks, where participants were required to train models
from scratch using a reduced number of training samples. The primary objective
was to encourage novel approaches that incorporate relevant inductive biases to
enhance the data efficiency of deep learning models. To foster creativity and
exploration, participants were strictly prohibited from utilizing pre-trained
checkpoints and other transfer learning techniques. Significant advancements
were made compared to the provided baselines, where winning solutions surpassed
the baselines by a considerable margin in all four tasks. These achievements
were primarily attributed to the effective utilization of extensive data
augmentation policies, model ensembling techniques, and the implementation of
data-efficient training methods, including self-supervised representation
learning. This report highlights the key aspects of the challenges and their
outcomes.
|
[
"cs.CV"
] | false |
2305.19743
|
2023-05-31T11:09:37Z
|
Towards Monocular Shape from Refraction
|
[
"Antonin Sulc",
"Imari Sato",
"Bastian Goldluecke",
"Tali Treibitz"
] |
Refraction is a common physical phenomenon and has long been researched in
computer vision. Objects imaged through a refractive object appear distorted in
the image as a function of the shape of the interface between the media. This
hinders many computer vision applications, but can be utilized for obtaining
the geometry of the refractive interface. Previous approaches for refractive
surface recovery largely relied on various priors or additional information
like multiple images of the analyzed surface. In contrast, we claim that a
simple energy function based on Snell's law enables the reconstruction of an
arbitrary refractive surface geometry using just a single image and known
background texture and geometry. In the case of a single point, Snell's law has
two degrees of freedom, therefore to estimate a surface depth, we need
additional information. We show that solving for an entire surface at once
introduces implicit parameter-free spatial regularization and yields convincing
results when an intelligent initial guess is provided. We demonstrate our
approach through simulations and real-world experiments, where the
reconstruction shows encouraging results in the single-frame monocular setting.
|
[
"cs.CV"
] | false |
2305.19812
|
2023-05-31T12:54:51Z
|
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
|
[
"Aoran Xiao",
"Xiaoqin Zhang",
"Ling Shao",
"Shijian Lu"
] |
In the past decade, deep neural networks have achieved significant progress
in point cloud learning. However, collecting large-scale precisely-annotated
training data is extremely laborious and expensive, which hinders the
scalability of existing point cloud datasets and poses a bottleneck for
efficient exploration of point cloud data in various tasks and applications.
Label-efficient learning offers a promising solution by enabling effective deep
network training with much-reduced annotation efforts. This paper presents the
first comprehensive survey of label-efficient learning of point clouds. We
address three critical questions in this emerging research field: i) the
importance and urgency of label-efficient learning in point cloud processing,
ii) the subfields it encompasses, and iii) the progress achieved in this area.
To achieve this, we propose a taxonomy that organizes label-efficient learning
methods based on the data prerequisites provided by different types of labels.
We categorize four typical label-efficient learning approaches that
significantly reduce point cloud annotation efforts: data augmentation, domain
transfer learning, weakly-supervised learning, and pretrained foundation
models. For each approach, we outline the problem setup and provide an
extensive literature review that showcases relevant progress and challenges.
Finally, we share insights into current research challenges and potential
future directions. A project associated with this survey has been built at
\url{https://github.com/xiaoaoran/3D_label_efficient_learning}.
|
[
"cs.CV"
] | false |
2305.19844
|
2023-05-31T13:32:27Z
|
Learning Task-preferred Inference Routes for Gradient De-conflict in
Multi-output DNNs
|
[
"Yi Sun",
"Xin Xu",
"Jian Li",
"Xiaochang Hu",
"Yifei Shi",
"Ling-Li Zeng"
] |
Multi-output deep neural networks(MONs) contain multiple task branches, and
these tasks usually share partial network filters that lead to the entanglement
of different task inference routes. Due to the inconsistent optimization
objectives, the task gradients used for training MONs will interfere with each
other on the shared routes, which will decrease the overall model performance.
To address this issue, we propose a novel gradient de-conflict algorithm named
DR-MGF(Dynamic Routes and Meta-weighted Gradient Fusion) in this work.
Different from existing de-conflict methods, DR-MGF achieves gradient
de-conflict in MONs by learning task-preferred inference routes. The proposed
method is motivated by our experimental findings: the shared filters are not
equally important to different tasks. By designing the learnable task-specific
importance variables, DR-MGF evaluates the importance of filters for different
tasks. Through making the dominances of tasks over filters be proportional to
the task-specific importance of filters, DR-MGF can effectively reduce the
inter-task interference. The task-specific importance variables ultimately
determine task-preferred inference routes at the end of training iterations.
Extensive experimental results on CIFAR, ImageNet, and NYUv2 illustrate that
DR-MGF outperforms the existing de-conflict methods both in prediction accuracy
and convergence speed of MONs. Furthermore, DR-MGF can be extended to general
MONs without modifying the overall network structures.
|
[
"cs.CV"
] | false |
2305.19879
|
2023-05-31T14:14:21Z
|
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental
Segmentation
|
[
"Subhankar Roy",
"Riccardo Volpi",
"Gabriela Csurka",
"Diane Larlus"
] |
Class-incremental semantic image segmentation assumes multiple model updates,
each enriching the model to segment new categories. This is typically carried
out by providing expensive pixel-level annotations to the training algorithm
for all new objects, limiting the adoption of such methods in practical
applications. Approaches that solely require image-level labels offer an
attractive alternative, yet, such coarse annotations lack precise information
about the location and boundary of the new objects. In this paper we argue
that, since classes represent not just indices but semantic entities, the
conceptual relationships between them can provide valuable information that
should be leveraged. We propose a weakly supervised approach that exploits such
semantic relations to transfer objectness prior from the previously learned
classes into the new ones, complementing the supervisory signal from
image-level labels. We validate our approach on a number of continual learning
tasks, and show how even a simple pairwise interaction between classes can
significantly improve the segmentation mask quality of both old and new
classes. We show these conclusions still hold for longer and, hence, more
realistic sequences of tasks and for a challenging few-shot scenario.
|
[
"cs.CV"
] | false |
2305.19949
|
2023-05-31T15:33:57Z
|
Treasure in Distribution: A Domain Randomization based Multi-Source
Domain Generalization for 2D Medical Image Segmentation
|
[
"Ziyang Chen",
"Yongsheng Pan",
"Yiwen Ye",
"Hengfei Cui",
"Yong Xia"
] |
Although recent years have witnessed the great success of convolutional
neural networks (CNNs) in medical image segmentation, the domain shift issue
caused by the highly variable image quality of medical images hinders the
deployment of CNNs in real-world clinical applications. Domain generalization
(DG) methods aim to address this issue by training a robust model on the source
domain, which has a strong generalization ability. Previously, many DG methods
based on feature-space domain randomization have been proposed, which, however,
suffer from the limited and unordered search space of feature styles. In this
paper, we propose a multi-source DG method called Treasure in Distribution
(TriD), which constructs an unprecedented search space to obtain the model with
strong robustness by randomly sampling from a uniform distribution. To learn
the domain-invariant representations explicitly, we further devise a
style-mixing strategy in our TriD, which mixes the feature styles by randomly
mixing the augmented and original statistics along the channel wise and can be
extended to other DG methods. Extensive experiments on two medical segmentation
tasks with different modalities demonstrate that our TriD achieves superior
generalization performance on unseen target-domain data. Code is available at
https://github.com/Chen-Ziyang/TriD.
|
[
"cs.CV"
] | false |
2305.19962
|
2023-05-31T15:49:12Z
|
GANDiffFace: Controllable Generation of Synthetic Datasets for Face
Recognition with Realistic Variations
|
[
"Pietro Melzi",
"Christian Rathgeb",
"Ruben Tolosana",
"Ruben Vera-Rodriguez",
"Dominik Lawatsch",
"Florian Domin",
"Maxim Schaubert"
] |
Face recognition systems have significantly advanced in recent years, driven
by the availability of large-scale datasets. However, several issues have
recently came up, including privacy concerns that have led to the
discontinuation of well-established public datasets. Synthetic datasets have
emerged as a solution, even though current synthesis methods present other
drawbacks such as limited intra-class variations, lack of realism, and unfair
representation of demographic groups. This study introduces GANDiffFace, a
novel framework for the generation of synthetic datasets for face recognition
that combines the power of Generative Adversarial Networks (GANs) and Diffusion
models to overcome the limitations of existing synthetic datasets. In
GANDiffFace, we first propose the use of GANs to synthesize highly realistic
identities and meet target demographic distributions. Subsequently, we
fine-tune Diffusion models with the images generated with GANs, synthesizing
multiple images of the same identity with a variety of accessories, poses,
expressions, and contexts. We generate multiple synthetic datasets by changing
GANDiffFace settings, and compare their mated and non-mated score distributions
with the distributions provided by popular real-world datasets for face
recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the
proposed GANDiffFace, in particular the use of Diffusion models to enhance the
(limited) intra-class variations provided by GANs towards the level of
real-world datasets.
|
[
"cs.CV"
] | false |
2305.20049
|
2023-05-31T17:22:24Z
|
A Unified Conditional Framework for Diffusion-based Image Restoration
|
[
"Yi Zhang",
"Xiaoyu Shi",
"Dasong Li",
"Xiaogang Wang",
"Jian Wang",
"Hongsheng Li"
] |
Diffusion Probabilistic Models (DPMs) have recently shown remarkable
performance in image generation tasks, which are capable of generating highly
realistic images. When adopting DPMs for image restoration tasks, the crucial
aspect lies in how to integrate the conditional information to guide the DPMs
to generate accurate and natural output, which has been largely overlooked in
existing works. In this paper, we present a unified conditional framework based
on diffusion models for image restoration. We leverage a lightweight UNet to
predict initial guidance and the diffusion model to learn the residual of the
guidance. By carefully designing the basic module and integration module for
the diffusion model block, we integrate the guidance and other auxiliary
conditional information into every block of the diffusion model to achieve
spatially-adaptive generation conditioning. To handle high-resolution images,
we propose a simple yet effective inter-step patch-splitting strategy to
produce arbitrary-resolution images without grid artifacts. We evaluate our
conditional framework on three challenging tasks: extreme low-light denoising,
deblurring, and JPEG restoration, demonstrating its significant improvements in
perceptual quality and the generalization to restoration tasks.
|
[
"cs.CV"
] | false |
2305.20058
|
2023-05-31T17:33:28Z
|
Exploring Regions of Interest: Visualizing Histological Image
Classification for Breast Cancer using Deep Learning
|
[
"Imane Nedjar",
"Mohammed Brahimi",
"Said Mahmoudi",
"Khadidja Abi Ayad",
"Mohammed Amine Chikh"
] |
Computer aided detection and diagnosis systems based on deep learning have
shown promising performance in breast cancer detection. However, there are
cases where the obtained results lack justification. In this study, our
objective is to highlight the regions of interest used by a convolutional
neural network (CNN) for classifying histological images as benign or
malignant. We compare these regions with the regions identified by
pathologists. To achieve this, we employed the VGG19 architecture and tested
three visualization methods: Gradient, LRP Z, and LRP Epsilon. Additionally, we
experimented with three pixel selection methods: Bins, K-means, and MeanShift.
Based on the results obtained, the Gradient visualization method and the
MeanShift selection method yielded satisfactory outcomes for visualizing the
images.
|
[
"cs.CV"
] | false |
2306.00075
|
2023-05-31T18:00:17Z
|
CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from
Aerial Videos
|
[
"Duo Lu",
"Eric Eaton",
"Matt Weg",
"Wei Wang",
"Steven Como",
"Jeffrey Wishart",
"Hongbin Yu",
"Yezhou Yang"
] |
Road traffic scene reconstruction from videos has been desirable by road
safety regulators, city planners, researchers, and autonomous driving
technology developers. However, it is expensive and unnecessary to cover every
mile of the road with cameras mounted on the road infrastructure. This paper
presents a method that can process aerial videos to vehicle trajectory data so
that a traffic scene can be automatically reconstructed and accurately
re-simulated using computers. On average, the vehicle localization error is
about 0.1 m to 0.3 m using a consumer-grade drone flying at 120 meters. This
project also compiles a dataset of 50 reconstructed road traffic scenes from
about 100 hours of aerial videos to enable various downstream traffic analysis
applications and facilitate further road traffic related research. The dataset
is available at https://github.com/duolu/CAROM.
|
[
"cs.CV"
] | false |
2306.00112
|
2023-05-31T18:37:02Z
|
Additional Positive Enables Better Representation Learning for Medical
Images
|
[
"Dewen Zeng",
"Yawen Wu",
"Xinrong Hu",
"Xiaowei Xu",
"Jingtong Hu",
"Yiyu Shi"
] |
This paper presents a new way to identify additional positive pairs for BYOL,
a state-of-the-art (SOTA) self-supervised learning framework, to improve its
representation learning ability. Unlike conventional BYOL which relies on only
one positive pair generated by two augmented views of the same image, we argue
that information from different images with the same label can bring more
diversity and variations to the target features, thus benefiting representation
learning. To identify such pairs without any label, we investigate TracIn, an
instance-based and computationally efficient influence function, for BYOL
training. Specifically, TracIn is a gradient-based method that reveals the
impact of a training sample on a test sample in supervised learning. We extend
it to the self-supervised learning setting and propose an efficient batch-wise
per-sample gradient computation method to estimate the pairwise TracIn to
represent the similarity of samples in the mini-batch during training. For each
image, we select the most similar sample from other images as the additional
positive and pull their features together with BYOL loss. Experimental results
on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate
that the proposed method can improve the classification performance compared to
other competitive baselines in both semi-supervised and transfer learning
settings.
|
[
"cs.CV"
] | false |
2306.00118
|
2023-05-31T18:45:02Z
|
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
|
[
"Angtian Wang",
"Wufei Ma",
"Alan Yuille",
"Adam Kortylewski"
] |
Human vision demonstrates higher robustness than current AI algorithms under
out-of-distribution scenarios. It has been conjectured such robustness benefits
from performing analysis-by-synthesis. Our paper formulates triple vision tasks
in a consistent manner using approximate analysis-by-synthesis by
render-and-compare algorithms on neural features. In this work, we introduce
Neural Textured Deformable Meshes, which involve the object model with
deformable geometry that allows optimization on both camera parameters and
object geometries. The deformable mesh is parameterized as a neural field, and
covered by whole-surface neural texture maps, which are trained to have spatial
discriminability. During inference, we extract the feature map of the test
image and subsequently optimize the 3D pose and shape parameters of our model
using differentiable rendering to best reconstruct the target feature map. We
show that our analysis-by-synthesis is much more robust than conventional
neural networks when evaluated on real-world images and even in challenging
out-of-distribution scenarios, such as occlusion and domain shift. Our
algorithms are competitive with standard algorithms when tested on conventional
performance measures.
|
[
"cs.CV"
] | false |
2306.00129
|
2023-05-31T19:06:05Z
|
Self-supervised Vision Transformers for 3D Pose Estimation of Novel
Objects
|
[
"Stefan Thalhammer",
"Jean-Baptiste Weibel",
"Markus Vincze",
"Jose Garcia-Rodriguez"
] |
Object pose estimation is important for object manipulation and scene
understanding. In order to improve the general applicability of pose
estimators, recent research focuses on providing estimates for novel objects,
that is objects unseen during training. Such works use deep template matching
strategies to retrieve the closest template connected to a query image. This
template retrieval implicitly provides object class and pose. Despite the
recent success and improvements of Vision Transformers over CNNs for many
vision tasks, the state of the art uses CNN-based approaches for novel object
pose estimation. This work evaluates and demonstrates the differences between
self-supervised CNNs and Vision Transformers for deep template matching. In
detail, both types of approaches are trained using contrastive learning to
match training images against rendered templates of isolated objects. At test
time, such templates are matched against query images of known and novel
objects under challenging settings, such as clutter, occlusion and object
symmetries, using masked cosine similarity. The presented results not only
demonstrate that Vision Transformers improve in matching accuracy over CNNs,
but also that for some cases pre-trained Vision Transformers do not need
fine-tuning to do so. Furthermore, we highlight the differences in optimization
and network architecture when comparing these two types of network for deep
template matching.
|
[
"cs.CV"
] | false |
2306.00150
|
2023-05-31T19:46:18Z
|
Enrichment of the NLST and NSCLC-Radiomics computed tomography
collections with AI-derived annotations
|
[
"Deepa Krishnaswamy",
"Dennis Bontempi",
"Vamsi Thiriveedhi",
"Davide Punzo",
"David Clunie",
"Christopher P Bridge",
"Hugo JWL Aerts",
"Ron Kikinis",
"Andrey Fedorov"
] |
Public imaging datasets are critical for the development and evaluation of
automated tools in cancer imaging. Unfortunately, many do not include
annotations or image-derived features, complicating their downstream analysis.
Artificial intelligence-based annotation tools have been shown to achieve
acceptable performance and thus can be used to automatically annotate large
datasets. As part of the effort to enrich public data available within NCI
Imaging Data Commons (IDC), here we introduce AI-generated annotations for two
collections of computed tomography images of the chest, NSCLC-Radiomics, and
the National Lung Screening Trial. Using publicly available AI algorithms we
derived volumetric annotations of thoracic organs at risk, their corresponding
radiomics features, and slice-level annotations of anatomical landmarks and
regions. The resulting annotations are publicly available within IDC, where the
DICOM format is used to harmonize the data and achieve FAIR principles. The
annotations are accompanied by cloud-enabled notebooks demonstrating their use.
This study reinforces the need for large, publicly accessible curated datasets
and demonstrates how AI can be used to aid in cancer imaging.
|
[
"cs.CV"
] | false |
2306.00200
|
2023-05-31T21:39:02Z
|
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
|
[
"Jiashun Wang",
"Xueting Li",
"Sifei Liu",
"Shalini De Mello",
"Orazio Gallo",
"Xiaolong Wang",
"Jan Kautz"
] |
Transferring the pose of a reference avatar to stylized 3D characters of
various shapes is a fundamental task in computer graphics. Existing methods
either require the stylized characters to be rigged, or they use the stylized
character in the desired pose as ground truth at training. We present a
zero-shot approach that requires only the widely available deformed
non-stylized avatars in training, and deforms stylized characters of
significantly different shapes at inference. Classical methods achieve strong
generalization by deforming the mesh at the triangle level, but this requires
labelled correspondences. We leverage the power of local deformation, but
without requiring explicit correspondence labels. We introduce a
semi-supervised shape-understanding module to bypass the need for explicit
correspondences at test time, and an implicit pose deformation module that
deforms individual surface points to match the target pose. Furthermore, to
encourage realistic and accurate deformation of stylized characters, we
introduce an efficient volume-based test-time training procedure. Because it
does not need rigging, nor the deformed stylized character at training time,
our model generalizes to categories with scarce annotation, such as stylized
quadrupeds. Extensive experiments demonstrate the effectiveness of the proposed
method compared to the state-of-the-art approaches trained with comparable or
more supervision. Our project page is available at
https://jiashunwang.github.io/ZPT
|
[
"cs.CV"
] | false |
2306.00231
|
2023-05-31T23:01:11Z
|
A Universal Latent Fingerprint Enhancer Using Transformers
|
[
"Andre Brasil Vieira Wyzykowski",
"Anil K. Jain"
] |
Forensic science heavily relies on analyzing latent fingerprints, which are
crucial for criminal investigations. However, various challenges, such as
background noise, overlapping prints, and contamination, make the
identification process difficult. Moreover, limited access to real crime scene
and laboratory-generated databases hinders the development of efficient
recognition algorithms. This study aims to develop a fast method, which we call
ULPrint, to enhance various latent fingerprint types, including those obtained
from real crime scenes and laboratory-created samples, to boost fingerprint
recognition system performance. In closed-set identification accuracy
experiments, the enhanced image was able to improve the performance of the
MSU-AFIS from 61.56\% to 75.19\% in the NIST SD27 database, from 67.63\% to
77.02\% in the MSP Latent database, and from 46.90\% to 52.12\% in the NIST
SD302 database. Our contributions include (1) the development of a two-step
latent fingerprint enhancement method that combines Ridge Segmentation with
UNet and Mix Visual Transformer (MiT) SegFormer-B5 encoder architecture, (2)
the implementation of multiple dilated convolutions in the UNet architecture to
capture intricate, non-local patterns better and enhance ridge segmentation,
and (3) the guided blending of the predicted ridge mask with the latent
fingerprint. This novel approach, ULPrint, streamlines the enhancement process,
addressing challenges across diverse latent fingerprint types to improve
forensic investigations and criminal justice outcomes.
|
[
"cs.CV"
] | false |
2306.00238
|
2023-05-31T23:18:21Z
|
Bytes Are All You Need: Transformers Operating Directly On File Bytes
|
[
"Maxwell Horton",
"Sachin Mehta",
"Ali Farhadi",
"Mohammad Rastegari"
] |
Modern deep learning approaches usually transform inputs into a
modality-specific form. For example, the most common deep learning approach to
image classification involves decoding image file bytes into an RGB tensor
which is passed into a neural network. Instead, we investigate performing
classification directly on file bytes, without the need for decoding files at
inference time. Using file bytes as model inputs enables the development of
models which can operate on multiple input modalities. Our model,
\emph{ByteFormer}, achieves an ImageNet Top-1 classification accuracy of
$77.33\%$ when training and testing directly on TIFF file bytes using a
transformer backbone with configuration similar to DeiT-Ti ($72.2\%$ accuracy
when operating on RGB images). Without modifications or hyperparameter tuning,
ByteFormer achieves $95.42\%$ classification accuracy when operating on WAV
files from the Speech Commands v2 dataset (compared to state-of-the-art
accuracy of $98.7\%$). Additionally, we demonstrate that ByteFormer has
applications in privacy-preserving inference. ByteFormer is capable of
performing inference on particular obfuscated input representations with no
loss of accuracy. We also demonstrate ByteFormer's ability to perform inference
with a hypothetical privacy-preserving camera which avoids forming full images
by consistently masking $90\%$ of pixel channels, while still achieving
$71.35\%$ accuracy on ImageNet. Our code will be made available at
https://github.com/apple/ml-cvnets/tree/main/examples/byteformer.
|
[
"cs.CV"
] | true |
2306.00241
|
2023-05-31T23:27:07Z
|
Balancing Reconstruction and Editing Quality of GAN Inversion for Real
Image Editing with StyleGAN Prior Latent Space
|
[
"Kai Katsumata",
"Duc Minh Vo",
"Bei Liu",
"Hideki Nakayama"
] |
The exploration of the latent space in StyleGANs and GAN inversion exemplify
impressive real-world image editing, yet the trade-off between reconstruction
quality and editing quality remains an open problem. In this study, we revisit
StyleGANs' hyperspherical prior $\mathcal{Z}$ and $\mathcal{Z}^+$ and integrate
them into seminal GAN inversion methods to improve editing quality. Besides
faithful reconstruction, our extensions achieve sophisticated editing quality
with the aid of the StyleGAN prior. We project the real images into the
proposed space to obtain the inverted codes, by which we then move along
$\mathcal{Z}^{+}$, enabling semantic editing without sacrificing image quality.
Comprehensive experiments show that $\mathcal{Z}^{+}$ can replace the most
commonly-used $\mathcal{W}$, $\mathcal{W}^{+}$, and $\mathcal{S}$ spaces while
preserving reconstruction quality, resulting in reduced distortion of edited
images.
|
[
"cs.CV"
] | false |
2306.00246
|
2023-05-31T23:40:47Z
|
Fine-Grained Property Value Assessment using Probabilistic
Disaggregation
|
[
"Cohen Archbold",
"Benjamin Brodie",
"Aram Ansary Ogholbake",
"Nathan Jacobs"
] |
The monetary value of a given piece of real estate, a parcel, is often
readily available from a geographic information system. However, for many
applications, such as insurance and urban planning, it is useful to have
estimates of property value at much higher spatial resolutions. We propose a
method to estimate the distribution over property value at the pixel level from
remote sensing imagery. We evaluate on a real-world dataset of a major urban
area. Our results show that the proposed approaches are capable of generating
fine-level estimates of property values, significantly improving upon a diverse
collection of baseline approaches.
|
[
"cs.CV"
] | false |
2306.06069
|
2023-05-31T14:35:02Z
|
Gemtelligence: Accelerating Gemstone classification with Deep Learning
|
[
"Tommaso Bendinelli",
"Luca Biggio",
"Daniel Nyfeler",
"Abhigyan Ghosh",
"Peter Tollan",
"Moritz Alexander Kirschmann",
"Olga Fink"
] |
The value of luxury goods, particularly investment-grade gemstones, is
greatly influenced by their origin and authenticity, sometimes resulting in
differences worth millions of dollars. Traditionally, human experts have
determined the origin and detected treatments on gemstones through visual
inspections and a range of analytical methods. However, the interpretation of
the data can be subjective and time-consuming, resulting in inconsistencies. In
this study, we propose Gemtelligence, a novel approach based on deep learning
that enables accurate and consistent origin determination and treatment
detection. Gemtelligence comprises convolutional and attention-based neural
networks that process heterogeneous data types collected by multiple
instruments. Notably, the algorithm demonstrated comparable predictive
performance to expensive laser-ablation inductively-coupled-plasma
mass-spectrometry (ICP-MS) analysis and visual examination by human experts,
despite using input data from relatively inexpensive analytical methods. Our
innovative methodology represents a major breakthrough in the field of gemstone
analysis by significantly improving the automation and robustness of the entire
analytical process pipeline.
|
[
"cs.CV"
] | false |
2306.09351
|
2023-05-31T04:08:57Z
|
BN-DRISHTI: Bangla Document Recognition through Instance-level
Segmentation of Handwritten Text Images
|
[
"Sheikh Mohammad Jubaer",
"Nazifa Tabassum",
"Md. Ataur Rahman",
"Mohammad Khairul Islam"
] |
Handwriting recognition remains challenging for some of the most spoken
languages, like Bangla, due to the complexity of line and word segmentation
brought by the curvilinear nature of writing and lack of quality datasets. This
paper solves the segmentation problem by introducing a state-of-the-art method
(BN-DRISHTI) that combines a deep learning-based object detection framework
(YOLO) with Hough and Affine transformation for skew correction. However,
training deep learning models requires a massive amount of data. Thus, we also
present an extended version of the BN-HTRd dataset comprising 786 full-page
handwritten Bangla document images, line and word-level annotation for
segmentation, and corresponding ground truths for word recognition. Evaluation
on the test portion of our dataset resulted in an F-score of 99.97% for line
and 98% for word segmentation. For comparative analysis, we used three external
Bangla handwritten datasets, namely BanglaWriting, WBSUBNdb_text, and ICDAR
2013, where our system outperformed by a significant margin, further justifying
the performance of our approach on completely unseen samples.
|
[
"cs.CV"
] | false |
2305.19467
|
2023-05-31T00:32:00Z
|
Synthetic CT Generation from MRI using 3D Transformer-based Denoising
Diffusion Model
|
[
"Shaoyan Pan",
"Elham Abouei",
"Jacob Wynne",
"Tonghe Wang",
"Richard L. J. Qiu",
"Yuheng Li",
"Chih-Wei Chang",
"Junbo Peng",
"Justin Roper",
"Pretesh Patel",
"David S. Yu",
"Hui Mao",
"Xiaofeng Yang"
] |
Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT)
simplifies radiation therapy treatment planning by eliminating the need for CT
simulation and error-prone image registration, ultimately reducing patient
radiation dose and setup uncertainty. We propose an MRI-to-CT transformer-based
denoising diffusion probabilistic model (MC-DDPM) to transform MRI into
high-quality sCT to facilitate radiation treatment planning. MC-DDPM implements
diffusion processes with a shifted-window transformer network to generate sCT
from MRI. The proposed model consists of two processes: a forward process which
adds Gaussian noise to real CT scans, and a reverse process in which a
shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans
conditioned on the MRI from the same patient to produce noise-free CT scans.
With an optimally trained Swin-Vnet, the reverse diffusion process was used to
generate sCT scans matching MRI anatomy. We evaluated the proposed method by
generating sCT from MRI on a brain dataset and a prostate dataset. Qualitative
evaluation was performed using the mean absolute error (MAE) of Hounsfield unit
(HU), peak signal to noise ratio (PSNR), multi-scale Structure Similarity index
(MS-SSIM) and normalized cross correlation (NCC) indexes between ground truth
CTs and sCTs. MC-DDPM generated brain sCTs with state-of-the-art quantitative
results with MAE 43.317 HU, PSNR 27.046 dB, SSIM 0.965, and NCC 0.983. For the
prostate dataset, MC-DDPM achieved MAE 59.953 HU, PSNR 26.920 dB, SSIM 0.849,
and NCC 0.948. In conclusion, we have developed and validated a novel approach
for generating CT images from routine MRIs using a transformer-based DDPM. This
model effectively captures the complex relationship between CT and MRI images,
allowing for robust and high-quality synthetic CT (sCT) images to be generated
in minutes.
|
[
"eess.IV",
"cs.CV"
] | false |
2305.19492
|
2023-05-31T02:03:41Z
|
CVSNet: A Computer Implementation for Central Visual System of The Brain
|
[
"Ruimin Gao",
"Hao Zou",
"Zhekai Duan"
] |
In computer vision, different basic blocks are created around different
matrix operations, and models based on different basic blocks have achieved
good results. Good results achieved in vision tasks grants them rationality.
However, these experimental-based models also make deep learning long
criticized for principle and interpretability. Deep learning originated from
the concept of neurons in neuroscience, but recent designs detached natural
neural networks except for some simple concepts. In this paper, we build an
artificial neural network, CVSNet, which can be seen as a computer
implementation for central visual system of the brain. Each block in CVSNet
represents the same vision information as that in brains. In CVSNet, blocks
differs from each other and visual information flows through three independent
pathways and five different blocks. Thus CVSNet is completely different from
the design of all previous models, in which basic blocks are repeated to build
model and information between channels is mixed at the outset. In ablation
experiment, we show the information extracted by blocks in CVSNet and compare
with previous networks, proving effectiveness and rationality of blocks in
CVSNet from experiment side. And in the experiment of object recognition,
CVSNet achieves comparable results to ConvNets, Vision Transformers and MLPs.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.19498
|
2023-05-31T02:16:29Z
|
Perception and Semantic Aware Regularization for Sequential Confidence
Calibration
|
[
"Zhenghua Peng",
"Yu Luo",
"Tianshui Chen",
"Keke Xu",
"Shuangping Huang"
] |
Deep sequence recognition (DSR) models receive increasing attention due to
their superior application to various applications. Most DSR models use merely
the target sequences as supervision without considering other related
sequences, leading to over-confidence in their predictions. The DSR models
trained with label smoothing regularize labels by equally and independently
smoothing each token, reallocating a small value to other tokens for mitigating
overconfidence. However, they do not consider tokens/sequences correlations
that may provide more effective information to regularize training and thus
lead to sub-optimal performance. In this work, we find tokens/sequences with
high perception and semantic correlations with the target ones contain more
correlated and effective information and thus facilitate more effective
regularization. To this end, we propose a Perception and Semantic aware
Sequence Regularization framework, which explore perceptively and semantically
correlated tokens/sequences as regularization. Specifically, we introduce a
semantic context-free recognition and a language model to acquire similar
sequences with high perceptive similarities and semantic correlation,
respectively. Moreover, over-confidence degree varies across samples according
to their difficulties. Thus, we further design an adaptive calibration
intensity module to compute a difficulty score for each samples to obtain
finer-grained regularization. Extensive experiments on canonical sequence
recognition tasks, including scene text and speech recognition, demonstrate
that our method sets novel state-of-the-art results. Code is available at
https://github.com/husterpzh/PSSR.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.19671
|
2023-05-31T09:09:59Z
|
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
|
[
"Moritz Vandenhirtz",
"Laura Manduchi",
"Ričards Marcinkevičs",
"Julia E. Vogt"
] |
Spurious correlations are everywhere. While humans often do not perceive
them, neural networks are notorious for learning unwanted associations, also
known as biases, instead of the underlying decision rule. As a result,
practitioners are often unaware of the biased decision-making of their
classifiers. Such a biased model based on spurious correlations might not
generalize to unobserved data, leading to unintended, adverse consequences. We
propose Signal is Harder (SiH), a variational-autoencoder-based method that
simultaneously trains a biased and unbiased classifier using a novel,
disentangling reweighting scheme inspired by the focal loss. Using the unbiased
classifier, SiH matches or improves upon the performance of state-of-the-art
debiasing methods. To improve the interpretability of our technique, we propose
a perturbation scheme in the latent space for visualizing the bias that helps
practitioners become aware of the sources of spurious correlations.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.19821
|
2023-05-31T13:03:17Z
|
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented
Language Model Prompting
|
[
"Rita Ramos",
"Bruno Martins",
"Desmond Elliott"
] |
Multilingual image captioning has recently been tackled by training with
large-scale machine translated data, which is an expensive, noisy, and
time-consuming process. Without requiring any multilingual caption data, we
propose LMCap, an image-blind few-shot multilingual captioning model that works
by prompting a language model with retrieved captions. Specifically, instead of
following the standard encoder-decoder paradigm, given an image, LMCap first
retrieves the captions of similar images using a multilingual CLIP encoder.
These captions are then combined into a prompt for an XGLM decoder, in order to
generate captions in the desired language. In other words, the generation model
does not directly process the image, instead processing retrieved captions.
Experiments on the XM3600 dataset of geographically diverse images show that
our model is competitive with fully-supervised multilingual captioning models,
without requiring any supervised training on any captioning data.
|
[
"cs.CL",
"cs.CV"
] | false |
2305.19906
|
2023-05-31T14:38:35Z
|
Neural LerPlane Representations for Fast 4D Reconstruction of Deformable
Tissues
|
[
"Chen Yang",
"Kailing Wang",
"Yuehao Wang",
"Xiaokang Yang",
"Wei Shen"
] |
Reconstructing deformable tissues from endoscopic stereo videos in robotic
surgery is crucial for various clinical applications. However, existing methods
relying only on implicit representations are computationally expensive and
require dozens of hours, which limits further practical applications. To
address this challenge, we introduce LerPlane, a novel method for fast and
accurate reconstruction of surgical scenes under a single-viewpoint setting.
LerPlane treats surgical procedures as 4D volumes and factorizes them into
explicit 2D planes of static and dynamic fields, leading to a compact memory
footprint and significantly accelerated optimization. The efficient
factorization is accomplished by fusing features obtained through linear
interpolation of each plane and enables using lightweight neural networks to
model surgical scenes. Besides, LerPlane shares static fields, significantly
reducing the workload of dynamic tissue modeling. We also propose a novel
sample scheme to boost optimization and improve performance in regions with
tool occlusion and large motions. Experiments on DaVinci robotic surgery videos
demonstrate that LerPlane accelerates optimization by over 100$\times$ while
maintaining high quality across various non-rigid deformations, showing
significant promise for future intraoperative surgery applications.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.19937
|
2023-05-31T15:21:34Z
|
Breast Cancer Detection and Diagnosis: A comparative study of
state-of-the-arts deep learning architectures
|
[
"Brennon Maistry",
"Absalom E. Ezugwu"
] |
Breast cancer is a prevalent form of cancer among women, with over 1.5
million women being diagnosed each year. Unfortunately, the survival rates for
breast cancer patients in certain third-world countries, like South Africa, are
alarmingly low, with only 40% of diagnosed patients surviving beyond five
years. The inadequate availability of resources, including qualified
pathologists, delayed diagnoses, and ineffective therapy planning, contribute
to this low survival rate. To address this pressing issue, medical specialists
and researchers have turned to domain-specific AI approaches, specifically deep
learning models, to develop end-to-end solutions that can be integrated into
computer-aided diagnosis (CAD) systems. By improving the workflow of
pathologists, these AI models have the potential to enhance the detection and
diagnosis of breast cancer. This research focuses on evaluating the performance
of various cutting-edge convolutional neural network (CNN) architectures in
comparison to a relatively new model called the Vision Trans-former (ViT). The
objective is to determine the superiority of these models in terms of their
accuracy and effectiveness. The experimental results reveal that the ViT models
outperform the other selected state-of-the-art CNN architectures, achieving an
impressive accuracy rate of 95.15%. This study signifies a significant
advancement in the field, as it explores the utilization of data augmentation
and other relevant preprocessing techniques in conjunction with deep learning
models for the detection and diagnosis of breast cancer using datasets of
Breast Cancer Histopathological Image Classification.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.20006
|
2023-05-31T16:27:00Z
|
Physics-Informed Ensemble Representation for Light-Field Image
Super-Resolution
|
[
"Manchang Jin",
"Gaosheng Liu",
"Kunshu Hu",
"Xin Luo",
"Kun Li",
"Jingyu Yang"
] |
Recent learning-based approaches have achieved significant progress in light
field (LF) image super-resolution (SR) by exploring convolution-based or
transformer-based network structures. However, LF imaging has many intrinsic
physical priors that have not been fully exploited. In this paper, we analyze
the coordinate transformation of the LF imaging process to reveal the geometric
relationship in the LF images. Based on such geometric priors, we introduce a
new LF subspace of virtual-slit images (VSI) that provide sub-pixel information
complementary to sub-aperture images. To leverage the abundant correlation
across the four-dimensional data with manageable complexity, we propose
learning ensemble representation of all $C_4^2$ LF subspaces for more effective
feature extraction. To super-resolve image structures from undersampled LF
data, we propose a geometry-aware decoder, named EPIXformer, which constrains
the transformer's operational searching regions with a LF physical prior.
Experimental results on both spatial and angular SR tasks demonstrate that the
proposed method outperforms other state-of-the-art schemes, especially in
handling various disparities.
|
[
"eess.IV",
"cs.CV"
] | false |
2305.20047
|
2023-05-31T17:21:24Z
|
LOWA: Localize Objects in the Wild with Attributes
|
[
"Xiaoyuan Guo",
"Kezhen Chen",
"Jinmeng Rao",
"Yawen Zhang",
"Baochen Sun",
"Jie Yang"
] |
We present LOWA, a novel method for localizing objects with attributes
effectively in the wild. It aims to address the insufficiency of current
open-vocabulary object detectors, which are limited by the lack of
instance-level attribute classification and rare class names. To train LOWA, we
propose a hybrid vision-language training strategy to learn object detection
and recognition with class names as well as attribute information. With LOWA,
users can not only detect objects with class names, but also able to localize
objects by attributes. LOWA is built on top of a two-tower vision-language
architecture and consists of a standard vision transformer as the image encoder
and a similar transformer as the text encoder. To learn the alignment between
visual and text inputs at the instance level, we train LOWA with three training
steps: object-level training, attribute-aware learning, and free-text joint
training of objects and attributes. This hybrid training strategy first ensures
correct object detection, then incorporates instance-level attribute
information, and finally balances the object class and attribute sensitivity.
We evaluate our model performance of attribute classification and attribute
localization on the Open-Vocabulary Attribute Detection (OVAD) benchmark and
the Visual Attributes in the Wild (VAW) dataset, and experiments indicate
strong zero-shot performance. Ablation studies additionally demonstrate the
effectiveness of each training step of our approach.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.00031
|
2023-05-31T06:50:32Z
|
Morphological Classification of Radio Galaxies using Semi-Supervised
Group Equivariant CNNs
|
[
"Mir Sazzat Hossain",
"Sugandha Roy",
"K. M. B. Asad",
"Arshad Momen",
"Amin Ahsan Ali",
"M Ashraful Amin",
"A. K. M. Mahbubur Rahman"
] |
Out of the estimated few trillion galaxies, only around a million have been
detected through radio frequencies, and only a tiny fraction, approximately a
thousand, have been manually classified. We have addressed this disparity
between labeled and unlabeled images of radio galaxies by employing a
semi-supervised learning approach to classify them into the known
Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant
Convolutional Neural Network (G-CNN) was used as an encoder of the
state-of-the-art self-supervised methods SimCLR (A Simple Framework for
Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own
Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2),
enabling it to effectively learn the representation of globally oriented
feature maps. After representation learning, we trained a fully-connected
classifier and fine-tuned the trained encoder with labeled data. Our findings
demonstrate that our semi-supervised approach outperforms existing
state-of-the-art methods across several metrics, including cluster quality,
convergence rate, accuracy, precision, recall, and the F1-score. Moreover,
statistical significance testing via a t-test revealed that our method
surpasses the performance of a fully supervised G-CNN. This study emphasizes
the importance of semi-supervised learning in radio galaxy classification,
where labeled data are still scarce, but the prospects for discovery are
immense.
|
[
"astro-ph.IM",
"cs.CV"
] | false |
2306.00034
|
2023-05-31T08:22:41Z
|
Diagnosis and Prognosis of Head and Neck Cancer Patients using
Artificial Intelligence
|
[
"Ikboljon Sobirov"
] |
Cancer is one of the most life-threatening diseases worldwide, and head and
neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases
recorded each year. Clinicians use medical imaging modalities such as computed
tomography and positron emission tomography to detect the presence of a tumor,
and they combine that information with clinical data for patient prognosis. The
process is mostly challenging and time-consuming. Machine learning and deep
learning can automate these tasks to help clinicians with highly promising
results. This work studies two approaches for H&N tumor segmentation: (i)
exploration and comparison of vision transformer (ViT)-based and convolutional
neural network-based models; and (ii) proposal of a novel 2D perspective to
working with 3D data. Furthermore, this work proposes two new architectures for
the prognosis task. An ensemble of several models predicts patient outcomes
(which won the HECKTOR 2021 challenge prognosis task), and a ViT-based
framework concurrently performs patient outcome prediction and tumor
segmentation, which outperforms the ensemble model.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.00202
|
2023-05-31T21:45:34Z
|
Building Manufacturing Deep Learning Models with Minimal and Imbalanced
Training Data Using Domain Adaptation and Data Augmentation
|
[
"Adrian Shuai Li",
"Elisa Bertino",
"Rih-Teng Wu",
"Ting-Yan Wu"
] |
Deep learning (DL) techniques are highly effective for defect detection from
images. Training DL classification models, however, requires vast amounts of
labeled data which is often expensive to collect. In many cases, not only the
available training data is limited but may also imbalanced. In this paper, we
propose a novel domain adaptation (DA) approach to address the problem of
labeled training data scarcity for a target learning task by transferring
knowledge gained from an existing source dataset used for a similar learning
task. Our approach works for scenarios where the source dataset and the dataset
available for the target learning task have same or different feature spaces.
We combine our DA approach with an autoencoder-based data augmentation approach
to address the problem of imbalanced target datasets. We evaluate our combined
approach using image data for wafer defect prediction. The experiments show its
superior performance against other algorithms when the number of labeled
samples in the target dataset is significantly small and the target dataset is
imbalanced.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.06066
|
2023-05-31T10:00:45Z
|
Multi-level Cross-modal Feature Alignment via Contrastive Learning
towards Zero-shot Classification of Remote Sensing Image Scenes
|
[
"Chun Liu",
"Suqiang Ma",
"Zheng Li",
"Wei Yang",
"Zhigang Han"
] |
Zero-shot classification of image scenes which can recognize the image scenes
that are not seen in the training stage holds great promise of lowering the
dependence on large numbers of labeled samples. To address the zero-shot image
scene classification, the cross-modal feature alignment methods have been
proposed in recent years. These methods mainly focus on matching the visual
features of each image scene with their corresponding semantic descriptors in
the latent space. Less attention has been paid to the contrastive relationships
between different image scenes and different semantic descriptors. In light of
the challenge of large intra-class difference and inter-class similarity among
image scenes and the potential noisy samples, these methods are susceptible to
the influence of the instances which are far from these of the same classes and
close to these of other classes. In this work, we propose a multi-level
cross-modal feature alignment method via contrastive learning for zero-shot
classification of remote sensing image scenes. While promoting the
single-instance level positive alignment between each image scene with their
corresponding semantic descriptors, the proposed method takes the
cross-instance contrastive relationships into consideration,and learns to keep
the visual and semantic features of different classes in the latent space apart
from each other. Extensive experiments have been done to evaluate the
performance of the proposed method. The results show that our proposed method
outperforms state of the art methods for zero-shot remote sensing image scene
classification. All the code and data are available at github
https://github.com/masuqiang/MCFA-Pytorch
|
[
"cs.CV",
"cs.LG"
] | false |
2306.06074
|
2023-05-31T20:46:06Z
|
Improved flood mapping for efficient policy design by fusion of
Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and
infrastructure exposed to floods
|
[
"Usman Nazir",
"Muhammad Ahmad Waseem",
"Falak Sher Khan",
"Rabia Saeed",
"Syed Muhammad Hasan",
"Momin Uppal",
"Zubair Khalid"
] |
A reliable yet inexpensive tool for the estimation of flood water spread is
conducive for efficient disaster management. The application of optical and SAR
imagery in tandem provides a means of extended availability and enhanced
reliability of flood mapping. We propose a methodology to merge these two types
of imagery into a common data space and demonstrate its use in the
identification of affected populations and infrastructure for the 2022 floods
in Pakistan. The merging of optical and SAR data provides us with improved
observations in cloud-prone regions; that is then used to gain additional
insights into flood mapping applications. The use of open source datasets from
WorldPop and OSM for population and roads respectively makes the exercise
globally replicable. The integration of flood maps with spatial data on
population and infrastructure facilitates informed policy design. We have shown
that within the top five flood-affected districts in Sindh province, Pakistan,
the affected population accounts for 31 %, while the length of affected roads
measures 1410.25 km out of a total of 7537.96 km.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.06080
|
2023-05-31T06:16:40Z
|
Detection of Late Blight Disease in Tomato Leaf Using Image Processing
Techniques
|
[
"Muhammad Shoaib Farooq",
"Tabir Arif",
"Shamyla Riaz"
] |
=One of the most frequently farmed crops is the tomato crop. Late blight is
the most prevalent tomato disease in the world, and often causes a significant
reduction in the production of tomato crops. The importance of tomatoes as an
agricultural product necessitates early detection of late blight. It is
produced by the fungus Phytophthora. The earliest signs of late blight on
tomatoes are unevenly formed, water-soaked lesions on the leaves located on the
plant canopy's younger leave White cottony growth may appear in humid
environments evident on the undersides of the leaves that have been impacted.
Lesions increase as the disease proceeds, turning the leaves brown to shrivel
up and die. Using picture segmentation and the Multi-class SVM technique, late
blight disorder is discovered in this work. Image segmentation is employed for
separating damaged areas on leaves, and the Multi-class SVM method is used for
reliable disease categorization. 30 reputable studies were chosen from a total
of 2770 recognized papers. The primary goal of this study is to compile
cutting-edge research that identifies current research trends, problems, and
prospects for late blight detection. It also looks at current approaches for
applying image processing to diagnose and detect late blight. A suggested
taxonomy for late blight detection has also been provided. In the same way, a
model for the development of the solutions to problems is also presented.
Finally, the research gaps have been presented in terms of open issues for the
provision of future directions in image processing for the researchers.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.19538
|
2023-05-31T03:56:31Z
|
Automatic Illumination Spectrum Recovery
|
[
"Nariman Habili",
"Jeremy Oorloff",
"Lars Petersson"
] |
We develop a deep learning network to estimate the illumination spectrum of
hyperspectral images under various lighting conditions. To this end, a dataset,
IllumNet, was created. Images were captured using a Specim IQ camera under
various illumination conditions, both indoor and outdoor. Outdoor images were
captured in sunny, overcast, and shady conditions and at different times of the
day. For indoor images, halogen and LED light sources were used, as well as
mixed light sources, mainly halogen or LED and fluorescent. The ResNet18
network was employed in this study, but with the 2D kernel changed to a 3D
kernel to suit the spectral nature of the data. As well as fitting the actual
illumination spectrum well, the predicted illumination spectrum should also be
smooth, and this is achieved by the cubic smoothing spline error cost function.
Experimental results indicate that the trained model can infer an accurate
estimate of the illumination spectrum.
|
[
"cs.CV",
"cs.LG",
"eess.IV"
] | false |
2305.19550
|
2023-05-31T04:35:50Z
|
Spotlight Attention: Robust Object-Centric Learning With a Spatial
Locality Prior
|
[
"Ayush Chakravarthy",
"Trang Nguyen",
"Anirudh Goyal",
"Yoshua Bengio",
"Michael C. Mozer"
] |
The aim of object-centric vision is to construct an explicit representation
of the objects in a scene. This representation is obtained via a set of
interchangeable modules called \emph{slots} or \emph{object files} that compete
for local patches of an image. The competition has a weak inductive bias to
preserve spatial continuity; consequently, one slot may claim patches scattered
diffusely throughout the image. In contrast, the inductive bias of human vision
is strong, to the degree that attention has classically been described with a
spotlight metaphor. We incorporate a spatial-locality prior into
state-of-the-art object-centric vision models and obtain significant
improvements in segmenting objects in both synthetic and real-world datasets.
Similar to human visual attention, the combination of image content and spatial
constraints yield robust unsupervised object-centric learning, including less
sensitivity to model hyperparameters.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.19603
|
2023-05-31T07:17:32Z
|
Intelligible Lip-to-Speech Synthesis with Speech Units
|
[
"Jeongsoo Choi",
"Minsu Kim",
"Yong Man Ro"
] |
In this paper, we propose a novel Lip-to-Speech synthesis (L2S) framework,
for synthesizing intelligible speech from a silent lip movement video.
Specifically, to complement the insufficient supervisory signal of the previous
L2S model, we propose to use quantized self-supervised speech representations,
named speech units, as an additional prediction target for the L2S model.
Therefore, the proposed L2S model is trained to generate multiple targets,
mel-spectrogram and speech units. As the speech units are discrete while
mel-spectrogram is continuous, the proposed multi-target L2S model can be
trained with strong content supervision, without using text-labeled data.
Moreover, to accurately convert the synthesized mel-spectrogram into a
waveform, we introduce a multi-input vocoder that can generate a clear waveform
even from blurry and noisy mel-spectrogram by referring to the speech units.
Extensive experimental results confirm the effectiveness of the proposed method
in L2S.
|
[
"cs.SD",
"cs.CV",
"eess.AS"
] | false |
2305.19643
|
2023-05-31T08:21:17Z
|
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability
in Anomaly Detection through Automatic Diffusion Models
|
[
"Cosmin I. Bercea",
"Michael Neumayr",
"Daniel Rueckert",
"Julia A. Schnabel"
] |
The introduction of diffusion models in anomaly detection has paved the way
for more effective and accurate image reconstruction in pathologies. However,
the current limitations in controlling noise granularity hinder diffusion
models' ability to generalize across diverse anomaly types and compromise the
restoration of healthy tissues. To overcome these challenges, we propose
AutoDDPM, a novel approach that enhances the robustness of diffusion models.
AutoDDPM utilizes diffusion models to generate initial likelihood maps of
potential anomalies and seamlessly integrates them with the original image.
Through joint noised distribution re-sampling, AutoDDPM achieves harmonization
and in-painting effects. Our study demonstrates the efficacy of AutoDDPM in
replacing anomalous regions while preserving healthy tissues, considerably
surpassing diffusion models' limitations. It also contributes valuable insights
and analysis on the limitations of current diffusion models, promoting robust
and interpretable anomaly detection in medical imaging - an essential aspect of
building autonomous clinical decision systems with higher interpretability.
|
[
"cs.CV",
"cs.AI",
"eess.IV"
] | false |
2305.19664
|
2023-05-31T09:02:58Z
|
Unveiling Cross Modality Bias in Visual Question Answering: A Causal
View with Possible Worlds VQA
|
[
"Ali Vosoughi",
"Shijian Deng",
"Songyang Zhang",
"Yapeng Tian",
"Chenliang Xu",
"Jiebo Luo"
] |
To increase the generalization capability of VQA systems, many recent studies
have tried to de-bias spurious language or vision associations that shortcut
the question or image to the answer. Despite these efforts, the literature
fails to address the confounding effect of vision and language simultaneously.
As a result, when they reduce bias learned from one modality, they usually
increase bias from the other. In this paper, we first model a confounding
effect that causes language and vision bias simultaneously, then propose a
counterfactual inference to remove the influence of this effect. The model
trained in this strategy can concurrently and efficiently reduce vision and
language bias. To the best of our knowledge, this is the first work to reduce
biases resulting from confounding effects of vision and language in VQA,
leveraging causal explain-away relations. We accompany our method with an
explain-away strategy, pushing the accuracy of the questions with numerical
answers results compared to existing methods that have been an open problem.
The proposed method outperforms the state-of-the-art methods in VQA-CP v2
datasets.
|
[
"cs.CV",
"cs.CL",
"cs.MM"
] | false |
2305.19774
|
2023-05-31T12:07:08Z
|
Ambiguity in solving imaging inverse problems with deep learning based
operators
|
[
"Davide Evangelista",
"Elena Morotti",
"Elena Loli Piccolomini",
"James Nagy"
] |
In recent years, large convolutional neural networks have been widely used as
tools for image deblurring, because of their ability in restoring images very
precisely. It is well known that image deblurring is mathematically modeled as
an ill-posed inverse problem and its solution is difficult to approximate when
noise affects the data. Really, one limitation of neural networks for
deblurring is their sensitivity to noise and other perturbations, which can
lead to instability and produce poor reconstructions. In addition, networks do
not necessarily take into account the numerical formulation of the underlying
imaging problem, when trained end-to-end. In this paper, we propose some
strategies to improve stability without losing to much accuracy to deblur
images with deep-learning based methods. First, we suggest a very small neural
architecture, which reduces the execution time for training, satisfying a green
AI need, and does not extremely amplify noise in the computed image. Second, we
introduce a unified framework where a pre-processing step balances the lack of
stability of the following, neural network-based, step. Two different
pre-processors are presented: the former implements a strong parameter-free
denoiser, and the latter is a variational model-based regularized formulation
of the latent imaging problem. This framework is also formally characterized by
mathematical analysis. Numerical experiments are performed to verify the
accuracy and stability of the proposed approaches for image deblurring when
unknown or not-quantified noise is present; the results confirm that they
improve the network stability with respect to noise. In particular, the
model-based framework represents the most reliable trade-off between visual
precision and robustness.
|
[
"cs.CV",
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2305.19780
|
2023-05-31T12:13:45Z
|
A technique to jointly estimate depth and depth uncertainty for unmanned
aerial vehicles
|
[
"Michaël Fonder",
"Marc Van Droogenbroeck"
] |
When used by autonomous vehicles for trajectory planning or obstacle
avoidance, depth estimation methods need to be reliable. Therefore, estimating
the quality of the depth outputs is critical. In this paper, we show how
M4Depth, a state-of-the-art depth estimation method designed for unmanned
aerial vehicle (UAV) applications, can be enhanced to perform joint depth and
uncertainty estimation. For that, we present a solution to convert the
uncertainty estimates related to parallax generated by M4Depth into uncertainty
estimates related to depth, and show that it outperforms the standard
probabilistic approach. Our experiments on various public datasets demonstrate
that our method performs consistently, even in zero-shot transfer. Besides, our
method offers a compelling value when compared to existing multi-view depth
estimation methods as it performs similarly on a multi-view depth estimation
benchmark despite being 2.5 times faster and causal, as opposed to other
methods. The code of our method is publicly available at
https://github.com/michael-fonder/M4DepthU .
|
[
"cs.CV",
"cs.AI",
"cs.RO"
] | false |
2305.19896
|
2023-05-31T14:30:17Z
|
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for
HAR onto FPGAs
|
[
"Petros Toupas",
"Christos-Savvas Bouganis",
"Dimitrios Tzovaras"
] |
Surveillance systems, autonomous vehicles, human monitoring systems, and
video retrieval are just few of the many applications in which 3D Convolutional
Neural Networks are exploited. However, their extensive use is restricted by
their high computational and memory requirements, especially when integrated
into systems with limited resources. This study proposes a toolflow that
optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA
devices, taking into account FPGA resources and off-chip memory
characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs
to model the designs and introduces transformations to expand and explore the
design space, resulting in high-throughput designs. A variety of 3D CNN models
were evaluated using the proposed toolflow on multiple FPGA devices,
demonstrating its potential to deliver competitive performance compared to
earlier hand-tuned and model-specific designs.
|
[
"cs.AR",
"cs.AI",
"cs.CV",
"cs.LG"
] | false |
2305.19933
|
2023-05-31T15:17:28Z
|
Speaking the Language of Your Listener: Audience-Aware Adaptation via
Plug-and-Play Theory of Mind
|
[
"Ece Takmaz",
"Nicolo' Brandizzi",
"Mario Giulianelli",
"Sandro Pezzelle",
"Raquel Fernández"
] |
Dialogue participants may have varying levels of knowledge about the topic
under discussion. In such cases, it is essential for speakers to adapt their
utterances by taking their audience into account. Yet, it is an open question
how such adaptation can be modelled in computational agents. In this paper, we
model a visually grounded referential game between a knowledgeable speaker and
a listener with more limited visual and linguistic experience. Inspired by
psycholinguistic theories, we endow our speaker with the ability to adapt its
referring expressions via a simulation module that monitors the effectiveness
of planned utterances from the listener's perspective. We propose an adaptation
mechanism building on plug-and-play approaches to controlled language
generation, where utterance generation is steered on the fly by the simulator
without finetuning the speaker's underlying language model. Our results and
analyses show that our approach is effective: the speaker's utterances become
closer to the listener's domain of expertise, which leads to higher
communicative success.
|
[
"cs.CL",
"cs.AI",
"cs.CV"
] | false |
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