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2305.17633
|
2023-05-28T05:00:07Z
|
DPFormer: Learning Differentially Private Transformer on Long-Tailed
Data
|
[
"Youlong Ding",
"Xueyang Wu",
"Hao Wang",
"Weike Pan"
] |
The Transformer has emerged as a versatile and effective architecture with
broad applications. However, it still remains an open problem how to
efficiently train a Transformer model of high utility with differential privacy
guarantees. In this paper, we identify two key challenges in learning
differentially private Transformers, i.e., heavy computation overhead due to
per-sample gradient clipping and unintentional attention distraction within the
attention mechanism. In response, we propose DPFormer, equipped with Phantom
Clipping and Re-Attention Mechanism, to address these challenges. Our
theoretical analysis shows that DPFormer can reduce computational costs during
gradient clipping and effectively mitigate attention distraction (which could
obstruct the training process and lead to a significant performance drop,
especially in the presence of long-tailed data). Such analysis is further
corroborated by empirical results on two real-world datasets, demonstrating the
efficiency and effectiveness of the proposed DPFormer.
|
[
"cs.LG"
] | false |
2305.18382
|
2023-05-28T06:57:27Z
|
Adaptive Sparsity Level during Training for Efficient Time Series
Forecasting with Transformers
|
[
"Zahra Atashgahi",
"Mykola Pechenizkiy",
"Raymond Veldhuis",
"Decebal Constantin Mocanu"
] |
Efficient time series forecasting has become critical for real-world
applications, particularly with deep neural networks (DNNs). Efficiency in DNNs
can be achieved through sparse connectivity and reducing the model size.
However, finding the sparsity level automatically during training remains a
challenging task due to the heterogeneity in the loss-sparsity tradeoffs across
the datasets. In this paper, we propose \enquote{\textbf{P}runing with
\textbf{A}daptive \textbf{S}parsity \textbf{L}evel} (\textbf{PALS}), to
automatically seek an optimal balance between loss and sparsity, all without
the need for a predefined sparsity level. PALS draws inspiration from both
sparse training and during-training methods. It introduces the novel "expand"
mechanism in training sparse neural networks, allowing the model to dynamically
shrink, expand, or remain stable to find a proper sparsity level. In this
paper, we focus on achieving efficiency in transformers known for their
excellent time series forecasting performance but high computational cost.
Nevertheless, PALS can be applied directly to any DNN. In the scope of these
arguments, we demonstrate its effectiveness also on the DLinear model.
Experimental results on six benchmark datasets and five state-of-the-art
transformer variants show that PALS substantially reduces model size while
maintaining comparable performance to the dense model. More interestingly, PALS
even outperforms the dense model, in 12 and 14 cases out of 30 cases in terms
of MSE and MAE loss, respectively, while reducing 65% parameter count and 63%
FLOPs on average. Our code will be publicly available upon acceptance of the
paper.
|
[
"cs.LG"
] | false |
2305.18389
|
2023-05-28T10:53:34Z
|
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by
Random Labeling
|
[
"Mansour Zoubeirou A Mayaki",
"Michel Riveill"
] |
Anomaly detection or more generally outliers detection is one of the most
popular and challenging subject in theoretical and applied machine learning.
The main challenge is that in general we have access to very few labeled data
or no labels at all. In this paper, we present a new semi-supervised anomaly
detection method called \textbf{AnoRand} by combining a deep learning
architecture with random synthetic label generation. The proposed architecture
has two building blocks: (1) a noise detection (ND) block composed of feed
forward ferceptron and (2) an autoencoder (AE) block. The main idea of this new
architecture is to learn one class (e.g. the majority class in case of anomaly
detection) as well as possible by taking advantage of the ability of auto
encoders to represent data in a latent space and the ability of Feed Forward
Perceptron (FFP) to learn one class when the data is highly imbalanced. First,
we create synthetic anomalies by randomly disturbing (add noise) few samples
(e.g. 2\%) from the training set. Second, we use the normal and the synthetic
samples as input to our model. We compared the performance of the proposed
method to 17 state-of-the-art unsupervised anomaly detection method on
synthetic datasets and 57 real-world datasets. Our results show that this new
method generally outperforms most of the state-of-the-art methods and has the
best performance (AUC ROC and AUC PR) on the vast majority of reference
datasets. We also tested our method in a supervised way by using the actual
labels to train the model. The results show that it has very good performance
compared to most of state-of-the-art supervised algorithms.
|
[
"cs.LG"
] | false |
2305.17667
|
2023-05-28T09:06:44Z
|
Choose your Data Wisely: A Framework for Semantic Counterfactuals
|
[
"Edmund Dervakos",
"Konstantinos Thomas",
"Giorgos Filandrianos",
"Giorgos Stamou"
] |
Counterfactual explanations have been argued to be one of the most intuitive
forms of explanation. They are typically defined as a minimal set of edits on a
given data sample that, when applied, changes the output of a model on that
sample. However, a minimal set of edits is not always clear and understandable
to an end-user, as it could, for instance, constitute an adversarial example
(which is indistinguishable from the original data sample to an end-user).
Instead, there are recent ideas that the notion of minimality in the context of
counterfactuals should refer to the semantics of the data sample, and not to
the feature space. In this work, we build on these ideas, and propose a
framework that provides counterfactual explanations in terms of knowledge
graphs. We provide an algorithm for computing such explanations (given some
assumptions about the underlying knowledge), and quantitatively evaluate the
framework with a user study.
|
[
"cs.AI",
"cs.LG"
] | false |
2305.18374
|
2023-05-28T05:34:50Z
|
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for
Top-N Recommendation
|
[
"Edoardo D'Amico",
"Aonghus Lawlor",
"Neil Hurley"
] |
The use of graph convolution in the development of recommender system
algorithms has recently achieved state-of-the-art results in the collaborative
filtering task (CF). While it has been demonstrated that the graph convolution
operation is connected to a filtering operation on the graph spectral domain,
the theoretical rationale for why this leads to higher performance on the
collaborative filtering problem remains unknown. The presented work makes two
contributions. First, we investigate the effect of using graph convolution
throughout the user and item representation learning processes, demonstrating
how the latent features learned are pushed from the filtering operation into
the subspace spanned by the eigenvectors associated with the highest
eigenvalues of the normalised adjacency matrix, and how vectors lying on this
subspace are the optimal solutions for an objective function related to the sum
of the prediction function over the training data. Then, we present an approach
that directly leverages the eigenvectors to emulate the solution obtained
through graph convolution, eliminating the requirement for a time-consuming
gradient descent training procedure while also delivering higher performance on
three real-world datasets.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.18376
|
2023-05-28T05:56:47Z
|
Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors --
Algorithm and Application
|
[
"Jun-Gi Jang",
"Jeongyoung Lee",
"Yong-chan Park",
"U Kang"
] |
How can we efficiently and accurately analyze an irregular tensor in a
dual-way streaming setting where the sizes of two dimensions of the tensor
increase over time? What types of anomalies are there in the dual-way streaming
setting? An irregular tensor is a collection of matrices whose column lengths
are the same while their row lengths are different. In a dual-way streaming
setting, both new rows of existing matrices and new matrices arrive over time.
PARAFAC2 decomposition is a crucial tool for analyzing irregular tensors.
Although real-time analysis is necessary in the dual-way streaming, static
PARAFAC2 decomposition methods fail to efficiently work in this setting since
they perform PARAFAC2 decomposition for accumulated tensors whenever new data
arrive. Existing streaming PARAFAC2 decomposition methods work in a limited
setting and fail to handle new rows of matrices efficiently. In this paper, we
propose Dash, an efficient and accurate PARAFAC2 decomposition method working
in the dual-way streaming setting. When new data are given, Dash efficiently
performs PARAFAC2 decomposition by carefully dividing the terms related to old
and new data and avoiding naive computations involved with old data.
Furthermore, applying a forgetting factor makes Dash follow recent movements.
Extensive experiments show that Dash achieves up to 14.0x faster speed than
existing PARAFAC2 decomposition methods for newly arrived data. We also provide
discoveries for detecting anomalies in real-world datasets, including Subprime
Mortgage Crisis and COVID-19.
|
[
"cs.LG",
"cs.IR"
] | false |
2305.18380
|
2023-05-28T06:41:06Z
|
Potential-based Credit Assignment for Cooperative RL-based Testing of
Autonomous Vehicles
|
[
"Utku Ayvaz",
"Chih-Hong Cheng",
"Hao Shen"
] |
While autonomous vehicles (AVs) may perform remarkably well in generic
real-life cases, their irrational action in some unforeseen cases leads to
critical safety concerns. This paper introduces the concept of collaborative
reinforcement learning (RL) to generate challenging test cases for AV planning
and decision-making module. One of the critical challenges for collaborative RL
is the credit assignment problem, where a proper assignment of rewards to
multiple agents interacting in the traffic scenario, considering all parameters
and timing, turns out to be non-trivial. In order to address this challenge, we
propose a novel potential-based reward-shaping approach inspired by
counterfactual analysis for solving the credit-assignment problem. The
evaluation in a simulated environment demonstrates the superiority of our
proposed approach against other methods using local and global rewards.
|
[
"cs.LG",
"cs.SE"
] | false |
2305.18383
|
2023-05-28T08:09:25Z
|
A Three-regime Model of Network Pruning
|
[
"Yefan Zhou",
"Yaoqing Yang",
"Arin Chang",
"Michael W. Mahoney"
] |
Recent work has highlighted the complex influence training hyperparameters,
e.g., the number of training epochs, can have on the prunability of machine
learning models. Perhaps surprisingly, a systematic approach to predict
precisely how adjusting a specific hyperparameter will affect prunability
remains elusive. To address this gap, we introduce a phenomenological model
grounded in the statistical mechanics of learning. Our approach uses
temperature-like and load-like parameters to model the impact of neural network
(NN) training hyperparameters on pruning performance. A key empirical result we
identify is a sharp transition phenomenon: depending on the value of a
load-like parameter in the pruned model, increasing the value of a
temperature-like parameter in the pre-pruned model may either enhance or impair
subsequent pruning performance. Based on this transition, we build a
three-regime model by taxonomizing the global structure of the pruned NN loss
landscape. Our model reveals that the dichotomous effect of high temperature is
associated with transitions between distinct types of global structures in the
post-pruned model. Based on our results, we present three case-studies: 1)
determining whether to increase or decrease a hyperparameter for improved
pruning; 2) selecting the best model to prune from a family of models; and 3)
tuning the hyperparameter of the Sharpness Aware Minimization method for better
pruning performance.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.18385
|
2023-05-28T09:38:28Z
|
Self-attention Dual Embedding for Graphs with Heterophily
|
[
"Yurui Lai",
"Taiyan Zhang",
"Rui Fan"
] |
Graph Neural Networks (GNNs) have been highly successful for the node
classification task. GNNs typically assume graphs are homophilic, i.e.
neighboring nodes are likely to belong to the same class. However, a number of
real-world graphs are heterophilic, and this leads to much lower classification
accuracy using standard GNNs. In this work, we design a novel GNN which is
effective for both heterophilic and homophilic graphs. Our work is based on
three main observations. First, we show that node features and graph topology
provide different amounts of informativeness in different graphs, and therefore
they should be encoded independently and prioritized in an adaptive manner.
Second, we show that allowing negative attention weights when propagating graph
topology information improves accuracy. Finally, we show that asymmetric
attention weights between nodes are helpful. We design a GNN which makes use of
these observations through a novel self-attention mechanism. We evaluate our
algorithm on real-world graphs containing thousands to millions of nodes and
show that we achieve state-of-the-art results compared to existing GNNs. We
also analyze the effectiveness of the main components of our design on
different graphs.
|
[
"cs.LG",
"cs.SI"
] | false |
2305.18386
|
2023-05-28T09:46:18Z
|
A Synergistic Framework Leveraging Autoencoders and Generative
Adversarial Networks for the Synthesis of Computational Fluid Dynamics
Results in Aerofoil Aerodynamics
|
[
"Tanishk Nandal",
"Vaibhav Fulara",
"Raj Kumar Singh"
] |
In the realm of computational fluid dynamics (CFD), accurate prediction of
aerodynamic behaviour plays a pivotal role in aerofoil design and optimization.
This study proposes a novel approach that synergistically combines autoencoders
and Generative Adversarial Networks (GANs) for the purpose of generating CFD
results. Our innovative framework harnesses the intrinsic capabilities of
autoencoders to encode aerofoil geometries into a compressed and informative
20-length vector representation. Subsequently, a conditional GAN network
adeptly translates this vector into precise pressure-distribution plots,
accounting for fixed wind velocity, angle of attack, and turbulence level
specifications. The training process utilizes a meticulously curated dataset
acquired from JavaFoil software, encompassing a comprehensive range of aerofoil
geometries. The proposed approach exhibits profound potential in reducing the
time and costs associated with aerodynamic prediction, enabling efficient
evaluation of aerofoil performance. The findings contribute to the advancement
of computational techniques in fluid dynamics and pave the way for enhanced
design and optimization processes in aerodynamics.
|
[
"physics.flu-dyn",
"cs.LG"
] | false |
2305.18388
|
2023-05-28T10:52:46Z
|
The Statistical Benefits of Quantile Temporal-Difference Learning for
Value Estimation
|
[
"Mark Rowland",
"Yunhao Tang",
"Clare Lyle",
"Rémi Munos",
"Marc G. Bellemare",
"Will Dabney"
] |
We study the problem of temporal-difference-based policy evaluation in
reinforcement learning. In particular, we analyse the use of a distributional
reinforcement learning algorithm, quantile temporal-difference learning (QTD),
for this task. We reach the surprising conclusion that even if a practitioner
has no interest in the return distribution beyond the mean, QTD (which learns
predictions about the full distribution of returns) may offer performance
superior to approaches such as classical TD learning, which predict only the
mean return, even in the tabular setting.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.18393
|
2023-05-28T12:20:07Z
|
Training Private Models That Know What They Don't Know
|
[
"Stephan Rabanser",
"Anvith Thudi",
"Abhradeep Thakurta",
"Krishnamurthy Dvijotham",
"Nicolas Papernot"
] |
Training reliable deep learning models which avoid making overconfident but
incorrect predictions is a longstanding challenge. This challenge is further
exacerbated when learning has to be differentially private: protection provided
to sensitive data comes at the price of injecting additional randomness into
the learning process. In this work, we conduct a thorough empirical
investigation of selective classifiers -- that can abstain when they are unsure
-- under a differential privacy constraint. We find that several popular
selective prediction approaches are ineffective in a differentially private
setting as they increase the risk of privacy leakage. At the same time, we
identify that a recent approach that only uses checkpoints produced by an
off-the-shelf private learning algorithm stands out as particularly suitable
under DP. Further, we show that differential privacy does not just harm utility
but also degrades selective classification performance. To analyze this effect
across privacy levels, we propose a novel evaluation mechanism which isolate
selective prediction performance across model utility levels. Our experimental
results show that recovering the performance level attainable by non-private
models is possible but comes at a considerable coverage cost as the privacy
budget decreases.
|
[
"cs.LG",
"cs.CR"
] | false |
2305.18397
|
2023-05-28T13:17:51Z
|
Prediction of the 2023 Turkish Presidential Election Results Using
Social Media Data
|
[
"Aysun Bozanta",
"Fuad Bayrak",
"Ayse Basar"
] |
Social media platforms influence the way political campaigns are run and
therefore they have become an increasingly important tool for politicians to
directly interact with citizens. Previous elections in various countries have
shown that social media data may significantly impact election results. In this
study, we aim to predict the vote shares of parties participating in the 2023
elections in Turkey by combining social media data from various platforms
together with traditional polling data. Our approach is a volume-based approach
that considers the number of social media interactions rather than content. We
compare several prediction models across varying time windows. Our results show
that for all time windows, the ARIMAX model outperforms the other algorithms.
|
[
"cs.SI",
"cs.LG"
] | false |
2305.18412
|
2023-05-28T17:49:26Z
|
Short-term Temporal Dependency Detection under Heterogeneous Event
Dynamic with Hawkes Processes
|
[
"Yu Chen",
"Fengpei Li",
"Anderson Schneider",
"Yuriy Nevmyvaka",
"Asohan Amarasingham",
"Henry Lam"
] |
Many event sequence data exhibit mutually exciting or inhibiting patterns.
Reliable detection of such temporal dependency is crucial for scientific
investigation. The de facto model is the Multivariate Hawkes Process (MHP),
whose impact function naturally encodes a causal structure in Granger
causality. However, the vast majority of existing methods use direct or
nonlinear transform of standard MHP intensity with constant baseline,
inconsistent with real-world data. Under irregular and unknown heterogeneous
intensity, capturing temporal dependency is hard as one struggles to
distinguish the effect of mutual interaction from that of intensity
fluctuation. In this paper, we address the short-term temporal dependency
detection issue. We show the maximum likelihood estimation (MLE) for
cross-impact from MHP has an error that can not be eliminated but may be
reduced by order of magnitude, using heterogeneous intensity not of the target
HP but of the interacting HP. Then we proposed a robust and
computationally-efficient method modified from MLE that does not rely on the
prior estimation of the heterogeneous intensity and is thus applicable in a
data-limited regime (e.g., few-shot, no repeated observations). Extensive
experiments on various datasets show that our method outperforms existing ones
by notable margins, with highlighted novel applications in neuroscience.
|
[
"stat.AP",
"cs.LG"
] | false |
2305.18416
|
2023-05-28T19:07:25Z
|
Examining the Role and Limits of Batchnorm Optimization to Mitigate
Diverse Hardware-noise in In-memory Computing
|
[
"Abhiroop Bhattacharjee",
"Abhishek Moitra",
"Youngeun Kim",
"Yeshwanth Venkatesha",
"Priyadarshini Panda"
] |
In-Memory Computing (IMC) platforms such as analog crossbars are gaining
focus as they facilitate the acceleration of low-precision Deep Neural Networks
(DNNs) with high area- & compute-efficiencies. However, the intrinsic
non-idealities in crossbars, which are often non-deterministic and non-linear,
degrade the performance of the deployed DNNs. In addition to quantization
errors, most frequently encountered non-idealities during inference include
crossbar circuit-level parasitic resistances and device-level non-idealities
such as stochastic read noise and temporal drift. In this work, our goal is to
closely examine the distortions caused by these non-idealities on the
dot-product operations in analog crossbars and explore the feasibility of a
nearly training-less solution via crossbar-aware fine-tuning of batchnorm
parameters in real-time to mitigate the impact of the non-idealities. This
enables reduction in hardware costs in terms of memory and training energy for
IMC noise-aware retraining of the DNN weights on crossbars.
|
[
"cs.LG",
"cs.ET"
] | false |
2305.18421
|
2023-05-28T19:41:23Z
|
HyperTime: Hyperparameter Optimization for Combating Temporal
Distribution Shifts
|
[
"Shaokun Zhang",
"Yiran Wu",
"Zhonghua Zheng",
"Qingyun Wu",
"Chi Wang"
] |
In this work, we propose a hyperparameter optimization method named
\emph{HyperTime} to find hyperparameters robust to potential temporal
distribution shifts in the unseen test data. Our work is motivated by an
important observation that it is, in many cases, possible to achieve temporally
robust predictive performance via hyperparameter optimization. Based on this
observation, we leverage the `worst-case-oriented' philosophy from the robust
optimization literature to help find such robust hyperparameter configurations.
HyperTime imposes a lexicographic priority order on average validation loss and
worst-case validation loss over chronological validation sets. We perform a
theoretical analysis on the upper bound of the expected test loss, which
reveals the unique advantages of our approach. We also demonstrate the strong
empirical performance of the proposed method on multiple machine learning tasks
with temporal distribution shifts.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.18423
|
2023-05-28T20:32:16Z
|
On the Role of Noise in the Sample Complexity of Learning Recurrent
Neural Networks: Exponential Gaps for Long Sequences
|
[
"Alireza Fathollah Pour",
"Hassan Ashtiani"
] |
We consider the class of noisy multi-layered sigmoid recurrent neural
networks with $w$ (unbounded) weights for classification of sequences of length
$T$, where independent noise distributed according to $\mathcal{N}(0,\sigma^2)$
is added to the output of each neuron in the network. Our main result shows
that the sample complexity of PAC learning this class can be bounded by $O
(w\log(T/\sigma))$. For the non-noisy version of the same class (i.e.,
$\sigma=0$), we prove a lower bound of $\Omega (wT)$ for the sample complexity.
Our results indicate an exponential gap in the dependence of sample complexity
on $T$ for noisy versus non-noisy networks. Moreover, given the mild
logarithmic dependence of the upper bound on $1/\sigma$, this gap still holds
even for numerically negligible values of $\sigma$.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.18425
|
2023-05-28T21:10:22Z
|
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of
Weight Residuals
|
[
"Simo Ryu",
"Seunghyun Seo",
"Jaejun Yoo"
] |
In this paper, we present an efficient method for storing fine-tuned models
by leveraging the low-rank properties of weight residuals. Our key observation
is that weight residuals in large overparameterized models exhibit even
stronger low-rank characteristics. Based on this insight, we propose Efficient
Residual Encoding (ERE), a novel approach that achieves efficient storage of
fine-tuned model weights by approximating the low-rank weight residuals.
Furthermore, we analyze the robustness of weight residuals and push the limit
of storage efficiency by utilizing additional quantization and layer-wise rank
allocation. Our experimental results demonstrate that our method significantly
reduces memory footprint while preserving performance in various tasks and
modalities. We release our code.
|
[
"cs.LG",
"cs.AI",
"I.2.6"
] | false |
2305.18426
|
2023-05-28T21:44:25Z
|
Employing Explainable Artificial Intelligence (XAI) Methodologies to
Analyze the Correlation between Input Variables and Tensile Strength in
Additively Manufactured Samples
|
[
"Akshansh Mishra",
"Vijaykumar S Jatti"
] |
This research paper explores the impact of various input parameters,
including Infill percentage, Layer Height, Extrusion Temperature, and Print
Speed, on the resulting Tensile Strength in objects produced through additive
manufacturing. The main objective of this study is to enhance our understanding
of the correlation between the input parameters and Tensile Strength, as well
as to identify the key factors influencing the performance of the additive
manufacturing process. To achieve this objective, we introduced the utilization
of Explainable Artificial Intelligence (XAI) techniques for the first time,
which allowed us to analyze the data and gain valuable insights into the
system's behavior. Specifically, we employed SHAP (SHapley Additive
exPlanations), a widely adopted framework for interpreting machine learning
model predictions, to provide explanations for the behavior of a machine
learning model trained on the data. Our findings reveal that the Infill
percentage and Extrusion Temperature have the most significant influence on
Tensile Strength, while the impact of Layer Height and Print Speed is
relatively minor. Furthermore, we discovered that the relationship between the
input parameters and Tensile Strength is highly intricate and nonlinear, making
it difficult to accurately describe using simple linear models.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.18429
|
2023-05-28T22:43:16Z
|
Visual Knowledge Discovery with General Line Coordinates
|
[
"Lincoln Huber",
"Boris Kovalerchuk",
"Charles Recaido"
] |
Understanding black-box Machine Learning methods on multidimensional data is
a key challenge in Machine Learning. While many powerful Machine Learning
methods already exist, these methods are often unexplainable or perform poorly
on complex data. This paper proposes visual knowledge discovery approaches
based on several forms of lossless General Line Coordinates. These are an
expansion of the previously introduced General Line Coordinates Linear and
Dynamic Scaffolding Coordinates to produce, explain, and visualize non-linear
classifiers with explanation rules. To ensure these non-linear models and rules
are accurate, General Line Coordinates Linear also developed new interactive
visual knowledge discovery algorithms for finding worst-case validation splits.
These expansions are General Line Coordinates non-linear, interactive rules
linear, hyperblock rules linear, and worst-case linear. Experiments across
multiple benchmark datasets show that this visual knowledge discovery method
can compete with other visual and computational Machine Learning algorithms
while improving both interpretability and accuracy in linear and non-linear
classifications. Major benefits from these expansions consist of the ability to
build accurate and highly interpretable models and rules from hyperblocks, the
ability to analyze interpretability weaknesses in a model, and the input of
expert knowledge through interactive and human-guided visual knowledge
discovery methods.
|
[
"cs.LG",
"cs.HC"
] | false |
2305.18432
|
2023-05-28T23:44:15Z
|
Interactive Decision Tree Creation and Enhancement with Complete
Visualization for Explainable Modeling
|
[
"Boris Kovalerchuk Andrew Dunn",
"Alex Worland",
"Sridevi Wagle"
] |
To increase the interpretability and prediction accuracy of the Machine
Learning (ML) models, visualization of ML models is a key part of the ML
process. Decision Trees (DTs) are essential in machine learning (ML) because
they are used to understand many black box ML models including Deep Learning
models. In this research, two new methods for creation and enhancement with
complete visualizing Decision Trees as understandable models are suggested.
These methods use two versions of General Line Coordinates (GLC): Bended
Coordinates (BC) and Shifted Paired Coordinates (SPC). The Bended Coordinates
are a set of line coordinates, where each coordinate is bended in a threshold
point of the respective DT node. In SPC, each n-D point is visualized in a set
of shifted pairs of 2-D Cartesian coordinates as a directed graph. These new
methods expand and complement the capabilities of existing methods to visualize
DT models more completely. These capabilities allow us to observe and analyze:
(1) relations between attributes, (2) individual cases relative to the DT
structure, (3) data flow in the DT, (4) sensitivity of each split threshold in
the DT nodes, and (5) density of cases in parts of the n-D space. These
features are critical for DT models' performance evaluation and improvement by
domain experts and end users as they help to prevent overgeneralization and
overfitting of the models. The advantages of this methodology are illustrated
in the case studies on benchmark real-world datasets. The paper also
demonstrates how to generalize them for decision tree visualizations in
different General Line Coordinates.
|
[
"cs.LG",
"cs.HC"
] | false |
2306.05283
|
2023-05-28T22:21:31Z
|
A Method for Detecting Murmurous Heart Sounds based on Self-similar
Properties
|
[
"Dixon Vimalajeewa",
"Chihoon Lee",
"Brani Vidakovic"
] |
A heart murmur is an atypical sound produced by the flow of blood through the
heart. It can be a sign of a serious heart condition, so detecting heart
murmurs is critical for identifying and managing cardiovascular diseases.
However, current methods for identifying murmurous heart sounds do not fully
utilize the valuable insights that can be gained by exploring intrinsic
properties of heart sound signals. To address this issue, this study proposes a
new discriminatory set of multiscale features based on the self-similarity and
complexity properties of heart sounds, as derived in the wavelet domain.
Self-similarity is characterized by assessing fractal behaviors, while
complexity is explored by calculating wavelet entropy. We evaluated the
diagnostic performance of these proposed features for detecting murmurs using a
set of standard classifiers. When applied to a publicly available heart sound
dataset, our proposed wavelet-based multiscale features achieved comparable
performance to existing methods with fewer features. This suggests that
self-similarity and complexity properties in heart sounds could be potential
biomarkers for improving the accuracy of murmur detection.
|
[
"eess.SP",
"cs.LG"
] | false |
2307.13608
|
2023-05-28T12:51:42Z
|
Geometric Epitope and Paratope Prediction
|
[
"Marco Pegoraro",
"Clémentine Dominé",
"Emanuele Rodolà",
"Petar Veličković",
"Andreea Deac"
] |
Antibody-antigen interactions play a crucial role in identifying and
neutralizing harmful foreign molecules. In this paper, we investigate the
optimal representation for predicting the binding sites in the two molecules
and emphasize the importance of geometric information. Specifically, we compare
different geometric deep learning methods applied to proteins' inner (I-GEP)
and outer (O-GEP) structures. We incorporate 3D coordinates and spectral
geometric descriptors as input features to fully leverage the geometric
information. Our research suggests that surface-based models are more efficient
than other methods, and our O-GEP experiments have achieved state-of-the-art
results with significant performance improvements.
|
[
"q-bio.BM",
"cs.LG"
] | false |
2305.18375
|
2023-05-28T05:38:28Z
|
Learning to Jump: Thinning and Thickening Latent Counts for Generative
Modeling
|
[
"Tianqi Chen",
"Mingyuan Zhou"
] |
Learning to denoise has emerged as a prominent paradigm to design
state-of-the-art deep generative models for natural images. How to use it to
model the distributions of both continuous real-valued data and categorical
data has been well studied in recently proposed diffusion models. However, it
is found in this paper to have limited ability in modeling some other types of
data, such as count and non-negative continuous data, that are often highly
sparse, skewed, heavy-tailed, and/or overdispersed. To this end, we propose
learning to jump as a general recipe for generative modeling of various types
of data. Using a forward count thinning process to construct learning
objectives to train a deep neural network, it employs a reverse count
thickening process to iteratively refine its generation through that network.
We demonstrate when learning to jump is expected to perform comparably to
learning to denoise, and when it is expected to perform better. For example,
learning to jump is recommended when the training data is non-negative and
exhibits strong sparsity, skewness, heavy-tailedness, and/or heterogeneity.
|
[
"cs.LG",
"stat.ME",
"stat.ML"
] | false |
2305.18384
|
2023-05-28T09:17:48Z
|
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation
Study
|
[
"Yiqi Zhong",
"Xianming Liu",
"Deming Zhai",
"Junjun Jiang",
"Xiangyang Ji"
] |
Large amounts of incremental learning algorithms have been proposed to
alleviate the catastrophic forgetting issue arises while dealing with
sequential data on a time series. However, the adversarial robustness of
incremental learners has not been widely verified, leaving potential security
risks. Specifically, for poisoning-based backdoor attacks, we argue that the
nature of streaming data in IL provides great convenience to the adversary by
creating the possibility of distributed and cross-task attacks -- an adversary
can affect \textbf{any unknown} previous or subsequent task by data poisoning
\textbf{at any time or time series} with extremely small amount of backdoor
samples injected (e.g., $0.1\%$ based on our observations). To attract the
attention of the research community, in this paper, we empirically reveal the
high vulnerability of 11 typical incremental learners against poisoning-based
backdoor attack on 3 learning scenarios, especially the cross-task
generalization effect of backdoor knowledge, while the poison ratios range from
$5\%$ to as low as $0.1\%$. Finally, the defense mechanism based on activation
clustering is found to be effective in detecting our trigger pattern to
mitigate potential security risks.
|
[
"cs.CR",
"cs.AI",
"cs.LG"
] | false |
2305.18392
|
2023-05-28T11:48:36Z
|
Speech Intelligibility Assessment of Dysarthric Speech by using Goodness
of Pronunciation with Uncertainty Quantification
|
[
"Eun Jung Yeo",
"Kwanghee Choi",
"Sunhee Kim",
"Minhwa Chung"
] |
This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes
Uncertainty Quantification (UQ) for automatic speech intelligibility assessment
for dysarthric speech. Current GoP methods rely heavily on neural
network-driven overconfident predictions, which is unsuitable for assessing
dysarthric speech due to its significant acoustic differences from healthy
speech. To alleviate the problem, UQ techniques were used on GoP by 1)
normalizing the phoneme prediction (entropy, margin, maxlogit, logit-margin)
and 2) modifying the scoring function (scaling, prior normalization). As a
result, prior-normalized maxlogit GoP achieves the best performance, with a
relative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP for
English, Korean, and Tamil, respectively. Furthermore, phoneme analysis is
conducted to identify which phoneme scores significantly correlate with
intelligibility scores in each language.
|
[
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2305.18406
|
2023-05-28T15:48:01Z
|
A machine learning approach to the prediction of heat-transfer
coefficients in micro-channels
|
[
"Tullio Traverso",
"Francesco Coletti",
"Luca Magri",
"Tassos G. Karayiannis",
"Omar K. Matar"
] |
The accurate prediction of the two-phase heat transfer coefficient (HTC) as a
function of working fluids, channel geometries and process conditions is key to
the optimal design and operation of compact heat exchangers. Advances in
artificial intelligence research have recently boosted the application of
machine learning (ML) algorithms to obtain data-driven surrogate models for the
HTC. For most supervised learning algorithms, the task is that of a nonlinear
regression problem. Despite the fact that these models have been proven capable
of outperforming traditional empirical correlations, they have key limitations
such as overfitting the data, the lack of uncertainty estimation, and
interpretability of the results. To address these limitations, in this paper,
we use a multi-output Gaussian process regression (GPR) to estimate the HTC in
microchannels as a function of the mass flow rate, heat flux, system pressure
and channel diameter and length. The model is trained using the Brunel
Two-Phase Flow database of high-fidelity experimental data. The advantages of
GPR are data efficiency, the small number of hyperparameters to be trained
(typically of the same order of the number of input dimensions), and the
automatic trade-off between data fit and model complexity guaranteed by the
maximization of the marginal likelihood (Bayesian approach). Our paper proposes
research directions to improve the performance of the GPR-based model in
extrapolation.
|
[
"physics.flu-dyn",
"cs.LG",
"physics.data-an"
] | false |
2305.18407
|
2023-05-28T15:56:02Z
|
A Group Symmetric Stochastic Differential Equation Model for Molecule
Multi-modal Pretraining
|
[
"Shengchao Liu",
"Weitao Du",
"Zhiming Ma",
"Hongyu Guo",
"Jian Tang"
] |
Molecule pretraining has quickly become the go-to schema to boost the
performance of AI-based drug discovery. Naturally, molecules can be represented
as 2D topological graphs or 3D geometric point clouds. Although most existing
pertaining methods focus on merely the single modality, recent research has
shown that maximizing the mutual information (MI) between such two modalities
enhances the molecule representation ability. Meanwhile, existing molecule
multi-modal pretraining approaches approximate MI based on the representation
space encoded from the topology and geometry, thus resulting in the loss of
critical structural information of molecules. To address this issue, we propose
MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and
reflection-antisymmetric) stochastic differential equation models to generate
the 3D geometries from 2D topologies, and vice versa, directly in the input
space. It not only obtains tighter MI bound but also enables prosperous
downstream tasks than the previous work. By comparing with 17 pretraining
baselines, we empirically verify that MoleculeSDE can learn an expressive
representation with state-of-the-art performance on 26 out of 32 downstream
tasks.
|
[
"cs.LG",
"cs.AI",
"q-bio.BM"
] | false |
2305.18420
|
2023-05-28T19:40:46Z
|
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
|
[
"Shengbo Wang",
"Nian Si",
"Jose Blanchet",
"Zhengyuan Zhou"
] |
Dynamic decision making under distributional shifts is of fundamental
interest in theory and applications of reinforcement learning: The distribution
of the environment on which the data is collected can differ from that of the
environment on which the model is deployed. This paper presents two novel
model-free algorithms, namely the distributionally robust Q-learning and its
variance-reduced counterpart, that can effectively learn a robust policy
despite distributional shifts. These algorithms are designed to efficiently
approximate the $q$-function of an infinite-horizon $\gamma$-discounted robust
Markov decision process with Kullback-Leibler uncertainty set to an entry-wise
$\epsilon$-degree of precision. Further, the variance-reduced distributionally
robust Q-learning combines the synchronous Q-learning with variance-reduction
techniques to enhance its performance. Consequently, we establish that it
attains a minmax sample complexity upper bound of $\tilde
O(|S||A|(1-\gamma)^{-4}\epsilon^{-2})$, where $S$ and $A$ denote the state and
action spaces. This is the first complexity result that is independent of the
uncertainty size $\delta$, thereby providing new complexity theoretic insights.
Additionally, a series of numerical experiments confirm the theoretical
findings and the efficiency of the algorithms in handling distributional
shifts.
|
[
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.18431
|
2023-05-28T23:25:51Z
|
Optimizing Airbnb Search Journey with Multi-task Learning
|
[
"Chun How Tan",
"Austin Chan",
"Malay Haldar",
"Jie Tang",
"Xin Liu",
"Mustafa Abdool",
"Huiji Gao",
"Liwei He",
"Sanjeev Katariya"
] |
At Airbnb, an online marketplace for stays and experiences, guests often
spend weeks exploring and comparing multiple items before making a final
reservation request. Each reservation request may then potentially be rejected
or cancelled by the host prior to check-in. The long and exploratory nature of
the search journey, as well as the need to balance both guest and host
preferences, present unique challenges for Airbnb search ranking. In this
paper, we present Journey Ranker, a new multi-task deep learning model
architecture that addresses these challenges. Journey Ranker leverages
intermediate guest actions as milestones, both positive and negative, to better
progress the guest towards a successful booking. It also uses contextual
information such as guest state and search query to balance guest and host
preferences. Its modular and extensible design, consisting of four modules with
clear separation of concerns, allows for easy application to use cases beyond
the Airbnb search ranking context. We conducted offline and online testing of
the Journey Ranker and successfully deployed it in production to four different
Airbnb products with significant business metrics improvements.
|
[
"cs.IR",
"cs.AI",
"cs.LG"
] | false |
2305.18379
|
2023-05-28T06:33:37Z
|
Constrained Optimization via Exact Augmented Lagrangian and Randomized
Iterative Sketching
|
[
"Ilgee Hong",
"Sen Na",
"Michael W. Mahoney",
"Mladen Kolar"
] |
We consider solving equality-constrained nonlinear, nonconvex optimization
problems. This class of problems appears widely in a variety of applications in
machine learning and engineering, ranging from constrained deep neural
networks, to optimal control, to PDE-constrained optimization. We develop an
adaptive inexact Newton method for this problem class. In each iteration, we
solve the Lagrangian Newton system inexactly via a randomized iterative
sketching solver, and select a suitable stepsize by performing line search on
an exact augmented Lagrangian merit function. The randomized solvers have
advantages over deterministic linear system solvers by significantly reducing
per-iteration flops complexity and storage cost, when equipped with suitable
sketching matrices. Our method adaptively controls the accuracy of the
randomized solver and the penalty parameters of the exact augmented Lagrangian,
to ensure that the inexact Newton direction is a descent direction of the exact
augmented Lagrangian. This allows us to establish a global almost sure
convergence. We also show that a unit stepsize is admissible locally, so that
our method exhibits a local linear convergence. Furthermore, we prove that the
linear convergence can be strengthened to superlinear convergence if we
gradually sharpen the adaptive accuracy condition on the randomized solver. We
demonstrate the superior performance of our method on benchmark nonlinear
problems in CUTEst test set, constrained logistic regression with data from
LIBSVM, and a PDE-constrained problem.
|
[
"math.OC",
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | false |
2305.17852
|
2023-05-29T02:29:16Z
|
Hierarchical Neural Memory Network for Low Latency Event Processing
|
[
"Ryuhei Hamaguchi",
"Yasutaka Furukawa",
"Masaki Onishi",
"Ken Sakurada"
] |
This paper proposes a low latency neural network architecture for event-based
dense prediction tasks. Conventional architectures encode entire scene contents
at a fixed rate regardless of their temporal characteristics. Instead, the
proposed network encodes contents at a proper temporal scale depending on its
movement speed. We achieve this by constructing temporal hierarchy using
stacked latent memories that operate at different rates. Given low latency
event steams, the multi-level memories gradually extract dynamic to static
scene contents by propagating information from the fast to the slow memory
modules. The architecture not only reduces the redundancy of conventional
architectures but also exploits long-term dependencies. Furthermore, an
attention-based event representation efficiently encodes sparse event streams
into the memory cells. We conduct extensive evaluations on three event-based
dense prediction tasks, where the proposed approach outperforms the existing
methods on accuracy and latency, while demonstrating effective event and image
fusion capabilities. The code is available at https://hamarh.github.io/hmnet/
|
[
"cs.CV"
] | false |
2305.17858
|
2023-05-29T02:43:14Z
|
FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural
Rendering
|
[
"Yisu Zhang",
"Jianke Zhu",
"Lixiang Lin"
] |
Despite the promising results of multi-view reconstruction, the recent neural
rendering-based methods, such as implicit surface rendering (IDR) and volume
rendering (NeuS), not only incur a heavy computational burden on training but
also have the difficulties in disentangling the geometric and appearance.
Although having achieved faster training speed than implicit representation and
hash coding, the explicit voxel-based method obtains the inferior results on
recovering surface. To address these challenges, we propose an effective
mesh-based neural rendering approach, named FastMESH, which only samples at the
intersection of ray and mesh. A coarse-to-fine scheme is introduced to
efficiently extract the initial mesh by space carving. More importantly, we
suggest a hexagonal mesh model to preserve surface regularity by constraining
the second-order derivatives of vertices, where only low level of positional
encoding is engaged for neural rendering. The experiments demonstrate that our
approach achieves the state-of-the-art results on both reconstruction and novel
view synthesis. Besides, we obtain 10-fold acceleration on training comparing
to the implicit representation-based methods.
|
[
"cs.CV"
] | false |
2305.17861
|
2023-05-29T02:48:04Z
|
Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal
Action Localization
|
[
"Huan Ren",
"Wenfei Yang",
"Tianzhu Zhang",
"Yongdong Zhang"
] |
Weakly-supervised temporal action localization aims to localize and recognize
actions in untrimmed videos with only video-level category labels during
training. Without instance-level annotations, most existing methods follow the
Segment-based Multiple Instance Learning (S-MIL) framework, where the
predictions of segments are supervised by the labels of videos. However, the
objective for acquiring segment-level scores during training is not consistent
with the target for acquiring proposal-level scores during testing, leading to
suboptimal results. To deal with this problem, we propose a novel
Proposal-based Multiple Instance Learning (P-MIL) framework that directly
classifies the candidate proposals in both the training and testing stages,
which includes three key designs: 1) a surrounding contrastive feature
extraction module to suppress the discriminative short proposals by considering
the surrounding contrastive information, 2) a proposal completeness evaluation
module to inhibit the low-quality proposals with the guidance of the
completeness pseudo labels, and 3) an instance-level rank consistency loss to
achieve robust detection by leveraging the complementarity of RGB and FLOW
modalities. Extensive experimental results on two challenging benchmarks
including THUMOS14 and ActivityNet demonstrate the superior performance of our
method.
|
[
"cs.CV"
] | false |
2305.17863
|
2023-05-29T03:03:53Z
|
GridFormer: Residual Dense Transformer with Grid Structure for Image
Restoration in Adverse Weather Conditions
|
[
"Tao Wang",
"Kaihao Zhang",
"Ziqian Shao",
"Wenhan Luo",
"Bjorn Stenger",
"Tong Lu",
"Tae-Kyun Kim",
"Wei Liu",
"Hongdong Li"
] |
Image restoration in adverse weather conditions is a difficult task in
computer vision. In this paper, we propose a novel transformer-based framework
called GridFormer which serves as a backbone for image restoration under
adverse weather conditions. GridFormer is designed in a grid structure using a
residual dense transformer block, and it introduces two core designs. First, it
uses an enhanced attention mechanism in the transformer layer. The mechanism
includes stages of the sampler and compact self-attention to improve
efficiency, and a local enhancement stage to strengthen local information.
Second, we introduce a residual dense transformer block (RDTB) as the final
GridFormer layer. This design further improves the network's ability to learn
effective features from both preceding and current local features. The
GridFormer framework achieves state-of-the-art results on five diverse image
restoration tasks in adverse weather conditions, including image deraining,
dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The
source code and pre-trained models will be released.
|
[
"cs.CV"
] | false |
2305.17927
|
2023-05-29T07:42:10Z
|
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D
Annotations
|
[
"Yuexiong Ding",
"Xiaowei Luo"
] |
Currently, object detection applications in construction are almost based on
pure 2D data (both image and annotation are 2D-based), resulting in the
developed artificial intelligence (AI) applications only applicable to some
scenarios that only require 2D information. However, most advanced applications
usually require AI agents to perceive 3D spatial information, which limits the
further development of the current computer vision (CV) in construction. The
lack of 3D annotated datasets for construction object detection worsens the
situation. Therefore, this study creates and releases a virtual dataset with 3D
annotations named VCVW-3D, which covers 15 construction scenes and involves ten
categories of construction vehicles and workers. The VCVW-3D dataset is
characterized by multi-scene, multi-category, multi-randomness,
multi-viewpoint, multi-annotation, and binocular vision. Several typical 2D and
monocular 3D object detection models are then trained and evaluated on the
VCVW-3D dataset to provide a benchmark for subsequent research. The VCVW-3D is
expected to bring considerable economic benefits and practical significance by
reducing the costs of data construction, prototype development, and exploration
of space-awareness applications, thus promoting the development of CV in
construction, especially those of 3D applications.
|
[
"cs.CV"
] | false |
2305.17972
|
2023-05-29T09:30:39Z
|
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object
Detection
|
[
"Issa Mouawad",
"Nikolas Brasch",
"Fabian Manhardt",
"Federico Tombari",
"Francesca Odone"
] |
For autonomous vehicles, driving safely is highly dependent on the capability
to correctly perceive the environment in 3D space, hence the task of 3D object
detection represents a fundamental aspect of perception. While 3D sensors
deliver accurate metric perception, monocular approaches enjoy cost and
availability advantages that are valuable in a wide range of applications.
Unfortunately, training monocular methods requires a vast amount of annotated
data. Interestingly, self-supervised approaches have recently been successfully
applied to ease the training process and unlock access to widely available
unlabelled data. While related research leverages different priors including
LIDAR scans and stereo images, such priors again limit usability. Therefore, in
this work, we propose a novel approach to self-supervise 3D object detection
purely from RGB sequences alone, leveraging multi-view constraints and weak
labels. Our experiments on KITTI 3D dataset demonstrate performance on par with
state-of-the-art self-supervised methods using LIDAR scans or stereo images.
|
[
"cs.CV"
] | false |
2305.17997
|
2023-05-29T10:15:19Z
|
DiffRate : Differentiable Compression Rate for Efficient Vision
Transformers
|
[
"Mengzhao Chen",
"Wenqi Shao",
"Peng Xu",
"Mingbao Lin",
"Kaipeng Zhang",
"Fei Chao",
"Rongrong Ji",
"Yu Qiao",
"Ping Luo"
] |
Token compression aims to speed up large-scale vision transformers (e.g.
ViTs) by pruning (dropping) or merging tokens. It is an important but
challenging task. Although recent advanced approaches achieved great success,
they need to carefully handcraft a compression rate (i.e. number of tokens to
remove), which is tedious and leads to sub-optimal performance. To tackle this
problem, we propose Differentiable Compression Rate (DiffRate), a novel token
compression method that has several appealing properties prior arts do not
have. First, DiffRate enables propagating the loss function's gradient onto the
compression ratio, which is considered as a non-differentiable hyperparameter
in previous work. In this case, different layers can automatically learn
different compression rates layer-wisely without extra overhead. Second, token
pruning and merging can be naturally performed simultaneously in DiffRate,
while they were isolated in previous works. Third, extensive experiments
demonstrate that DiffRate achieves state-of-the-art performance. For example,
by applying the learned layer-wise compression rates to an off-the-shelf ViT-H
(MAE) model, we achieve a 40% FLOPs reduction and a 1.5x throughput
improvement, with a minor accuracy drop of 0.16% on ImageNet without
fine-tuning, even outperforming previous methods with fine-tuning. Codes and
models are available at https://github.com/OpenGVLab/DiffRate.
|
[
"cs.CV"
] | false |
2305.18013
|
2023-05-29T11:10:38Z
|
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place
Recognition
|
[
"Tiago Barros",
"Luís Garrote",
"Martin Aleksandrov",
"Cristiano Premebida",
"Urbano J. Nunes"
] |
Autonomous driving systems often require reliable loop closure detection to
guarantee reduced localization drift. Recently, 3D LiDAR-based localization
methods have used retrieval-based place recognition to find revisited places
efficiently. However, when deployed in challenging real-world scenarios, the
place recognition models become more complex, which comes at the cost of high
computational demand. This work tackles this problem from an
information-retrieval perspective, adopting a first-retrieve-then-re-ranking
paradigm, where an initial loop candidate ranking, generated from a 3D place
recognition model, is re-ordered by a proposed lightweight transformer-based
re-ranking approach (TReR). The proposed approach relies on global descriptors
only, being agnostic to the place recognition model. The experimental
evaluation, conducted on the KITTI Odometry dataset, where we compared TReR
with s.o.t.a. re-ranking approaches such as alphaQE and SGV, indicate the
robustness and efficiency when compared to alphaQE while offering a good
trade-off between robustness and efficiency when compared to SGV.
|
[
"cs.CV"
] | false |
2305.18047
|
2023-05-29T12:24:58Z
|
InstructEdit: Improving Automatic Masks for Diffusion-based Image
Editing With User Instructions
|
[
"Qian Wang",
"Biao Zhang",
"Michael Birsak",
"Peter Wonka"
] |
Recent works have explored text-guided image editing using diffusion models
and generated edited images based on text prompts. However, the models struggle
to accurately locate the regions to be edited and faithfully perform precise
edits. In this work, we propose a framework termed InstructEdit that can do
fine-grained editing based on user instructions. Our proposed framework has
three components: language processor, segmenter, and image editor. The first
component, the language processor, processes the user instruction using a large
language model. The goal of this processing is to parse the user instruction
and output prompts for the segmenter and captions for the image editor. We
adopt ChatGPT and optionally BLIP2 for this step. The second component, the
segmenter, uses the segmentation prompt provided by the language processor. We
employ a state-of-the-art segmentation framework Grounded Segment Anything to
automatically generate a high-quality mask based on the segmentation prompt.
The third component, the image editor, uses the captions from the language
processor and the masks from the segmenter to compute the edited image. We
adopt Stable Diffusion and the mask-guided generation from DiffEdit for this
purpose. Experiments show that our method outperforms previous editing methods
in fine-grained editing applications where the input image contains a complex
object or multiple objects. We improve the mask quality over DiffEdit and thus
improve the quality of edited images. We also show that our framework can
accept multiple forms of user instructions as input. We provide the code at
https://github.com/QianWangX/InstructEdit.
|
[
"cs.CV"
] | false |
2305.18063
|
2023-05-29T13:05:15Z
|
Vector-based Representation is the Key: A Study on Disentanglement and
Compositional Generalization
|
[
"Tao Yang",
"Yuwang Wang",
"Cuiling Lan",
"Yan Lu",
"Nanning Zheng"
] |
Recognizing elementary underlying concepts from observations
(disentanglement) and generating novel combinations of these concepts
(compositional generalization) are fundamental abilities for humans to support
rapid knowledge learning and generalize to new tasks, with which the deep
learning models struggle. Towards human-like intelligence, various works on
disentangled representation learning have been proposed, and recently some
studies on compositional generalization have been presented. However, few works
study the relationship between disentanglement and compositional
generalization, and the observed results are inconsistent. In this paper, we
study several typical disentangled representation learning works in terms of
both disentanglement and compositional generalization abilities, and we provide
an important insight: vector-based representation (using a vector instead of a
scalar to represent a concept) is the key to empower both good disentanglement
and strong compositional generalization. This insight also resonates the
neuroscience research that the brain encodes information in neuron population
activity rather than individual neurons. Motivated by this observation, we
further propose a method to reform the scalar-based disentanglement works
($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities.
We investigate the impact of the dimensions of vector-based representation and
one important question: whether better disentanglement indicates higher
compositional generalization. In summary, our study demonstrates that it is
possible to achieve both good concept recognition and novel concept
composition, contributing an important step towards human-like intelligence.
|
[
"cs.CV"
] | false |
2305.18076
|
2023-05-29T13:23:55Z
|
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via
Feature-Embedding Matching
|
[
"Tao Feng",
"Jie Zhang",
"Peizheng Wang",
"Zhijie Wang"
] |
The expenses involved in training state-of-the-art deep hashing retrieval
models have witnessed an increase due to the adoption of more sophisticated
models and large-scale datasets. Dataset Distillation (DD) or Dataset
Condensation(DC) focuses on generating smaller synthetic dataset that retains
the original information. Nevertheless, existing DD methods face challenges in
maintaining a trade-off between accuracy and efficiency. And the
state-of-the-art dataset distillation methods can not expand to all deep
hashing retrieval methods. In this paper, we propose an efficient condensation
framework that addresses these limitations by matching the feature-embedding
between synthetic set and real set. Furthermore, we enhance the diversity of
features by incorporating the strategies of early-stage augmented models and
multi-formation. Extensive experiments provide compelling evidence of the
remarkable superiority of our approach, both in terms of performance and
efficiency, compared to state-of-the-art baseline methods.
|
[
"cs.CV"
] | false |
2305.18078
|
2023-05-29T13:28:43Z
|
The mechanism underlying successful deep learning
|
[
"Yarden Tzach",
"Yuval Meir",
"Ofek Tevet",
"Ronit D. Gross",
"Shiri Hodassman",
"Roni Vardi",
"Ido Kanter"
] |
Deep architectures consist of tens or hundreds of convolutional layers (CLs)
that terminate with a few fully connected (FC) layers and an output layer
representing the possible labels of a complex classification task. According to
the existing deep learning (DL) rationale, the first CL reveals localized
features from the raw data, whereas the subsequent layers progressively extract
higher-level features required for refined classification. This article
presents an efficient three-phase procedure for quantifying the mechanism
underlying successful DL. First, a deep architecture is trained to maximize the
success rate (SR). Next, the weights of the first several CLs are fixed and
only the concatenated new FC layer connected to the output is trained,
resulting in SRs that progress with the layers. Finally, the trained FC weights
are silenced, except for those emerging from a single filter, enabling the
quantification of the functionality of this filter using a correlation matrix
between input labels and averaged output fields, hence a well-defined set of
quantifiable features is obtained. Each filter essentially selects a single
output label independent of the input label, which seems to prevent high SRs;
however, it counterintuitively identifies a small subset of possible output
labels. This feature is an essential part of the underlying DL mechanism and is
progressively sharpened with layers, resulting in enhanced signal-to-noise
ratios and SRs. Quantitatively, this mechanism is exemplified by the VGG-16,
VGG-6, and AVGG-16. The proposed mechanism underlying DL provides an accurate
tool for identifying each filter's quality and is expected to direct additional
procedures to improve the SR, computational complexity, and latency of DL.
|
[
"cs.CV"
] | false |
2305.18092
|
2023-05-29T13:51:41Z
|
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for
Image Deraining
|
[
"Zhiying Jiang",
"Risheng Liu",
"Shuzhou Yang",
"Zengxi Zhang",
"Xin Fan"
] |
Rain streaks significantly decrease the visibility of captured images and are
also a stumbling block that restricts the performance of subsequent computer
vision applications. The existing deep learning-based image deraining methods
employ manually crafted networks and learn a straightforward projection from
rainy images to clear images. In pursuit of better deraining performance, they
focus on elaborating a more complicated architecture rather than exploiting the
intrinsic properties of the positive and negative information. In this paper,
we propose a contrastive learning-based image deraining method that
investigates the correlation between rainy and clear images and leverages a
contrastive prior to optimize the mutual information of the rainy and restored
counterparts. Given the complex and varied real-world rain patterns, we develop
a recursive mechanism. It involves multi-scale feature extraction and dynamic
cross-level information recruitment modules. The former advances the portrayal
of diverse rain patterns more precisely, while the latter can selectively
compensate high-level features for shallow-level information. We term the
proposed recursive dynamic multi-scale network with a contrastive prior, RDMC.
Extensive experiments on synthetic benchmarks and real-world images demonstrate
that the proposed RDMC delivers strong performance on the depiction of rain
streaks and outperforms the state-of-the-art methods. Moreover, a practical
evaluation of object detection and semantic segmentation shows the
effectiveness of the proposed method.
|
[
"cs.CV"
] | false |
2305.18163
|
2023-05-29T15:49:20Z
|
Compact Real-time Radiance Fields with Neural Codebook
|
[
"Lingzhi Li",
"Zhongshu Wang",
"Zhen Shen",
"Li Shen",
"Ping Tan"
] |
Reconstructing neural radiance fields with explicit volumetric
representations, demonstrated by Plenoxels, has shown remarkable advantages on
training and rendering efficiency, while grid-based representations typically
induce considerable overhead for storage and transmission. In this work, we
present a simple and effective framework for pursuing compact radiance fields
from the perspective of compression methodology. By exploiting intrinsic
properties exhibiting in grid models, a non-uniform compression stem is
developed to significantly reduce model complexity and a novel parameterized
module, named Neural Codebook, is introduced for better encoding high-frequency
details specific to per-scene models via a fast optimization. Our approach can
achieve over 40 $\times$ reduction on grid model storage with competitive
rendering quality. In addition, the method can achieve real-time rendering
speed with 180 fps, realizing significant advantage on storage cost compared to
real-time rendering methods.
|
[
"cs.CV"
] | false |
2305.18264
|
2023-05-29T17:38:18Z
|
Gen-L-Video: Multi-Text to Long Video Generation via Temporal
Co-Denoising
|
[
"Fu-Yun Wang",
"Wenshuo Chen",
"Guanglu Song",
"Han-Jia Ye",
"Yu Liu",
"Hongsheng Li"
] |
Leveraging large-scale image-text datasets and advancements in diffusion
models, text-driven generative models have made remarkable strides in the field
of image generation and editing. This study explores the potential of extending
the text-driven ability to the generation and editing of multi-text conditioned
long videos. Current methodologies for video generation and editing, while
innovative, are often confined to extremely short videos (typically less than
24 frames) and are limited to a single text condition. These constraints
significantly limit their applications given that real-world videos usually
consist of multiple segments, each bearing different semantic information. To
address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video,
capable of extending off-the-shelf short video diffusion models for generating
and editing videos comprising hundreds of frames with diverse semantic segments
without introducing additional training, all while preserving content
consistency. We have implemented three mainstream text-driven video generation
and editing methodologies and extended them to accommodate longer videos imbued
with a variety of semantic segments with our proposed paradigm. Our
experimental outcomes reveal that our approach significantly broadens the
generative and editing capabilities of video diffusion models, offering new
possibilities for future research and applications. The code is available at
https://github.com/G-U-N/Gen-L-Video.
|
[
"cs.CV"
] | true |
2305.18476
|
2023-05-29T11:05:01Z
|
Explicit Visual Prompting for Universal Foreground Segmentations
|
[
"Weihuang Liu",
"Xi Shen",
"Chi-Man Pun",
"Xiaodong Cun"
] |
Foreground segmentation is a fundamental problem in computer vision, which
includes salient object detection, forgery detection, defocus blur detection,
shadow detection, and camouflage object detection. Previous works have
typically relied on domain-specific solutions to address accuracy and
robustness issues in those applications. In this paper, we present a unified
framework for a number of foreground segmentation tasks without any
task-specific designs. We take inspiration from the widely-used pre-training
and then prompt tuning protocols in NLP and propose a new visual prompting
model, named Explicit Visual Prompting (EVP). Different from the previous
visual prompting which is typically a dataset-level implicit embedding, our key
insight is to enforce the tunable parameters focusing on the explicit visual
content from each individual image, i.e., the features from frozen patch
embeddings and high-frequency components. Our method freezes a pre-trained
model and then learns task-specific knowledge using a few extra parameters.
Despite introducing only a small number of tunable parameters, EVP achieves
superior performance than full fine-tuning and other parameter-efficient
fine-tuning methods. Experiments in fourteen datasets across five tasks show
the proposed method outperforms other task-specific methods while being
considerably simple. The proposed method demonstrates the scalability in
different architectures, pre-trained weights, and tasks. The code is available
at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.
|
[
"cs.CV"
] | false |
2305.18557
|
2023-05-29T18:39:31Z
|
Evaluating 3D Shape Analysis Methods for Robustness to Rotation
Invariance
|
[
"Supriya Gadi Patil",
"Angel X. Chang",
"Manolis Savva"
] |
This paper analyzes the robustness of recent 3D shape descriptors to SO(3)
rotations, something that is fundamental to shape modeling. Specifically, we
formulate the task of rotated 3D object instance detection. To do so, we
consider a database of 3D indoor scenes, where objects occur in different
orientations. We benchmark different methods for feature extraction and
classification in the context of this task. We systematically contrast
different choices in a variety of experimental settings investigating the
impact on the performance of different rotation distributions, different
degrees of partial observations on the object, and the different levels of
difficulty of negative pairs. Our study, on a synthetic dataset of 3D scenes
where objects instances occur in different orientations, reveals that deep
learning-based rotation invariant methods are effective for relatively easy
settings with easy-to-distinguish pairs. However, their performance decreases
significantly when the difference in rotations on the input pair is large, or
when the degree of observation of input objects is reduced, or the difficulty
level of input pair is increased. Finally, we connect feature encodings
designed for rotation-invariant methods to 3D geometry that enable them to
acquire the property of rotation invariance.
|
[
"cs.CV"
] | false |
2305.17868
|
2023-05-29T03:17:03Z
|
NaturalFinger: Generating Natural Fingerprint with Generative
Adversarial Networks
|
[
"Kang Yang",
"Kunhao Lai"
] |
Deep neural network (DNN) models have become a critical asset of the model
owner as training them requires a large amount of resource (i.e. labeled data).
Therefore, many fingerprinting schemes have been proposed to safeguard the
intellectual property (IP) of the model owner against model extraction and
illegal redistribution. However, previous schemes adopt unnatural images as the
fingerprint, such as adversarial examples and noisy images, which can be easily
perceived and rejected by the adversary. In this paper, we propose
NaturalFinger which generates natural fingerprint with generative adversarial
networks (GANs). Besides, our proposed NaturalFinger fingerprints the decision
difference areas rather than the decision boundary, which is more robust. The
application of GAN not only allows us to generate more imperceptible samples,
but also enables us to generate unrestricted samples to explore the decision
boundary.To demonstrate the effectiveness of our fingerprint approach, we
evaluate our approach against four model modification attacks including
adversarial training and two model extraction attacks. Experiments show that
our approach achieves 0.91 ARUC value on the FingerBench dataset (154 models),
exceeding the optimal baseline (MetaV) over 17\%.
|
[
"cs.CV",
"cs.CR"
] | false |
2305.17895
|
2023-05-29T06:02:06Z
|
ReSup: Reliable Label Noise Suppression for Facial Expression
Recognition
|
[
"Xiang Zhang",
"Yan Lu",
"Huan Yan",
"Jingyang Huang",
"Yusheng Ji",
"Yu Gu"
] |
Because of the ambiguous and subjective property of the facial expression
recognition (FER) task, the label noise is widely existing in the FER dataset.
For this problem, in the training phase, current FER methods often directly
predict whether the label of the input image is noised or not, aiming to reduce
the contribution of the noised data in training. However, we argue that this
kind of method suffers from the low reliability of such noise data decision
operation. It makes that some mistakenly abounded clean data are not utilized
sufficiently and some mistakenly kept noised data disturbing the model learning
process. In this paper, we propose a more reliable noise-label suppression
method called ReSup (Reliable label noise Suppression for FER). First, instead
of directly predicting noised or not, ReSup makes the noise data decision by
modeling the distribution of noise and clean labels simultaneously according to
the disagreement between the prediction and the target. Specifically, to
achieve optimal distribution modeling, ReSup models the similarity distribution
of all samples. To further enhance the reliability of our noise decision
results, ReSup uses two networks to jointly achieve noise suppression.
Specifically, ReSup utilize the property that two networks are less likely to
make the same mistakes, making two networks swap decisions and tending to trust
decisions with high agreement. Extensive experiments on three popular
benchmarks show that the proposed method significantly outperforms
state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code:
https://github.com/purpleleaves007/FERDenoise
|
[
"cs.CV",
"cs.HC"
] | false |
2305.17898
|
2023-05-29T06:14:22Z
|
Convolutional neural network based on sparse graph attention mechanism
for MRI super-resolution
|
[
"Xin Hua",
"Zhijiang Du",
"Hongjian Yu",
"Jixin Maa"
] |
Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying
anatomical structures and aiding in accurate diagnosis. Medical image
super-resolution (SR) reconstruction using deep learning techniques can enhance
lesion analysis and assist doctors in improving diagnostic efficiency and
accuracy. However, existing deep learning-based SR methods predominantly rely
on convolutional neural networks (CNNs), which inherently limit the expressive
capabilities of these models and therefore make it challenging to discover
potential relationships between different image features. To overcome this
limitation, we propose an A-network that utilizes multiple convolution operator
feature extraction modules (MCO) for extracting image features using multiple
convolution operators. These extracted features are passed through multiple
sets of cross-feature extraction modules (MSC) to highlight key features
through inter-channel feature interactions, enabling subsequent feature
learning. An attention-based sparse graph neural network module is incorporated
to establish relationships between pixel features, learning which adjacent
pixels have the greatest impact on determining the features to be filled. To
evaluate our model's effectiveness, we conducted experiments using different
models on data generated from multiple datasets with different degradation
multiples, and the experimental results show that our method is a significant
improvement over the current state-of-the-art methods.
|
[
"cs.CV",
"cs.AI",
"I.4.5"
] | false |
2305.17932
|
2023-05-29T07:49:44Z
|
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion
Models
|
[
"Zhongxi Chen",
"Ke Sun",
"Xianming Lin",
"Rongrong Ji"
] |
Camouflaged Object Detection (COD) is a challenging task in computer vision
due to the high similarity between camouflaged objects and their surroundings.
Existing COD methods primarily employ semantic segmentation, which suffers from
overconfident incorrect predictions. In this paper, we propose a new paradigm
that treats COD as a conditional mask-generation task leveraging diffusion
models. Our method, dubbed CamoDiffusion, employs the denoising process of
diffusion models to iteratively reduce the noise of the mask. Due to the
stochastic sampling process of diffusion, our model is capable of sampling
multiple possible predictions from the mask distribution, avoiding the problem
of overconfident point estimation. Moreover, we develop specialized learning
strategies that include an innovative ensemble approach for generating robust
predictions and tailored forward diffusion methods for efficient training,
specifically for the COD task. Extensive experiments on three COD datasets
attest the superior performance of our model compared to existing
state-of-the-art methods, particularly on the most challenging COD10K dataset,
where our approach achieves 0.019 in terms of MAE.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17937
|
2023-05-29T08:00:54Z
|
Attention Mechanisms in Medical Image Segmentation: A Survey
|
[
"Yutong Xie",
"Bing Yang",
"Qingbiao Guan",
"Jianpeng Zhang",
"Qi Wu",
"Yong Xia"
] |
Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.
|
[
"eess.IV",
"cs.CV"
] | false |
2305.18008
|
2023-05-29T10:57:59Z
|
Pedestrian detection with high-resolution event camera
|
[
"Piotr Wzorek",
"Tomasz Kryjak"
] |
Despite the dynamic development of computer vision algorithms, the
implementation of perception and control systems for autonomous vehicles such
as drones and self-driving cars still poses many challenges. A video stream
captured by traditional cameras is often prone to problems such as motion blur
or degraded image quality due to challenging lighting conditions. In addition,
the frame rate - typically 30 or 60 frames per second - can be a limiting
factor in certain scenarios. Event cameras (DVS -- Dynamic Vision Sensor) are a
potentially interesting technology to address the above mentioned problems. In
this paper, we compare two methods of processing event data by means of deep
learning for the task of pedestrian detection. We used a representation in the
form of video frames, convolutional neural networks and asynchronous sparse
convolutional neural networks. The results obtained illustrate the potential of
event cameras and allow the evaluation of the accuracy and efficiency of the
methods used for high-resolution (1280 x 720 pixels) footage.
|
[
"cs.CV",
"eess.IV"
] | false |
2305.18033
|
2023-05-29T11:53:12Z
|
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer
Tissue
|
[
"Philippe Weitz",
"Masi Valkonen",
"Leslie Solorzano",
"Circe Carr",
"Kimmo Kartasalo",
"Constance Boissin",
"Sonja Koivukoski",
"Aino Kuusela",
"Dusan Rasic",
"Yanbo Feng",
"Sandra Sinius Pouplier",
"Abhinav Sharma",
"Kajsa Ledesma Eriksson",
"Stephanie Robertson",
"Christian Marzahl",
"Chandler D. Gatenbee",
"Alexander R. A. Anderson",
"Marek Wodzinski",
"Artur Jurgas",
"Niccolò Marini",
"Manfredo Atzori",
"Henning Müller",
"Daniel Budelmann",
"Nick Weiss",
"Stefan Heldmann",
"Johannes Lotz",
"Jelmer M. Wolterink",
"Bruno De Santi",
"Abhijeet Patil",
"Amit Sethi",
"Satoshi Kondo",
"Satoshi Kasai",
"Kousuke Hirasawa",
"Mahtab Farrokh",
"Neeraj Kumar",
"Russell Greiner",
"Leena Latonen",
"Anne-Vibeke Laenkholm",
"Johan Hartman",
"Pekka Ruusuvuori",
"Mattias Rantalainen"
] |
The alignment of tissue between histopathological whole-slide-images (WSI) is
crucial for research and clinical applications. Advances in computing, deep
learning, and availability of large WSI datasets have revolutionised WSI
analysis. Therefore, the current state-of-the-art in WSI registration is
unclear. To address this, we conducted the ACROBAT challenge, based on the
largest WSI registration dataset to date, including 4,212 WSIs from 1,152
breast cancer patients. The challenge objective was to align WSIs of tissue
that was stained with routine diagnostic immunohistochemistry to its
H&E-stained counterpart. We compare the performance of eight WSI registration
algorithms, including an investigation of the impact of different WSI
properties and clinical covariates. We find that conceptually distinct WSI
registration methods can lead to highly accurate registration performances and
identify covariates that impact performances across methods. These results
establish the current state-of-the-art in WSI registration and guide
researchers in selecting and developing methods.
|
[
"eess.IV",
"cs.CV"
] | false |
2305.18070
|
2023-05-29T13:17:20Z
|
Forensic Video Steganalysis in Spatial Domain by Noise Residual
Convolutional Neural Network
|
[
"Mart Keizer",
"Zeno Geradts",
"Meike Kombrink"
] |
This research evaluates a convolutional neural network (CNN) based approach
to forensic video steganalysis. A video steganography dataset is created to
train a CNN to conduct forensic steganalysis in the spatial domain. We use a
noise residual convolutional neural network to detect embedded secrets since a
steganographic embedding process will always result in the modification of
pixel values in video frames. Experimental results show that the CNN-based
approach can be an effective method for forensic video steganalysis and can
reach a detection rate of 99.96%. Keywords: Forensic, Steganalysis, Deep
Steganography, MSU StegoVideo, Convolutional Neural Networks
|
[
"cs.CV",
"cs.CR"
] | false |
2305.18277
|
2023-05-29T17:49:58Z
|
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
|
[
"Achraf Ben-Hamadou",
"Oussama Smaoui",
"Ahmed Rekik",
"Sergi Pujades",
"Edmond Boyer",
"Hoyeon Lim",
"Minchang Kim",
"Minkyung Lee",
"Minyoung Chung",
"Yeong-Gil Shin",
"Mathieu Leclercq",
"Lucia Cevidanes",
"Juan Carlos Prieto",
"Shaojie Zhuang",
"Guangshun Wei",
"Zhiming Cui",
"Yuanfeng Zhou",
"Tudor Dascalu",
"Bulat Ibragimov",
"Tae-Hoon Yong",
"Hong-Gi Ahn",
"Wan Kim",
"Jae-Hwan Han",
"Byungsun Choi",
"Niels van Nistelrooij",
"Steven Kempers",
"Shankeeth Vinayahalingam",
"Julien Strippoli",
"Aurélien Thollot",
"Hugo Setbon",
"Cyril Trosset",
"Edouard Ladroit"
] |
Teeth localization, segmentation, and labeling from intra-oral 3D scans are
essential tasks in modern dentistry to enhance dental diagnostics, treatment
planning, and population-based studies on oral health. However, developing
automated algorithms for teeth analysis presents significant challenges due to
variations in dental anatomy, imaging protocols, and limited availability of
publicly accessible data. To address these challenges, the 3DTeethSeg'22
challenge was organized in conjunction with the International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022,
with a call for algorithms tackling teeth localization, segmentation, and
labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans
from 900 patients was prepared, and each tooth was individually annotated by a
human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this
dataset. In this study, we present the evaluation results of the 3DTeethSeg'22
challenge. The 3DTeethSeg'22 challenge code can be accessed at:
https://github.com/abenhamadou/3DTeethSeg22_challenge
|
[
"cs.CV",
"cs.AI"
] | false |
2305.18279
|
2023-05-29T17:50:33Z
|
Contextual Object Detection with Multimodal Large Language Models
|
[
"Yuhang Zang",
"Wei Li",
"Jun Han",
"Kaiyang Zhou",
"Chen Change Loy"
] |
Recent Multimodal Large Language Models (MLLMs) are remarkable in
vision-language tasks, such as image captioning and question answering, but
lack the essential perception ability, i.e., object detection. In this work, we
address this limitation by introducing a novel research problem of contextual
object detection -- understanding visible objects within different human-AI
interactive contexts. Three representative scenarios are investigated,
including the language cloze test, visual captioning, and question answering.
Moreover, we present ContextDET, a unified multimodal model that is capable of
end-to-end differentiable modeling of visual-language contexts, so as to
locate, identify, and associate visual objects with language inputs for
human-AI interaction. Our ContextDET involves three key submodels: (i) a visual
encoder for extracting visual representations, (ii) a pre-trained LLM for
multimodal context decoding, and (iii) a visual decoder for predicting bounding
boxes given contextual object words. The new generate-then-detect framework
enables us to detect object words within human vocabulary. Extensive
experiments show the advantages of ContextDET on our proposed CODE benchmark,
open-vocabulary detection, and referring image segmentation. Github:
https://github.com/yuhangzang/ContextDET.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.18286
|
2023-05-29T17:56:13Z
|
Photoswap: Personalized Subject Swapping in Images
|
[
"Jing Gu",
"Yilin Wang",
"Nanxuan Zhao",
"Tsu-Jui Fu",
"Wei Xiong",
"Qing Liu",
"Zhifei Zhang",
"He Zhang",
"Jianming Zhang",
"HyunJoon Jung",
"Xin Eric Wang"
] |
In an era where images and visual content dominate our digital landscape, the
ability to manipulate and personalize these images has become a necessity.
Envision seamlessly substituting a tabby cat lounging on a sunlit window sill
in a photograph with your own playful puppy, all while preserving the original
charm and composition of the image. We present Photoswap, a novel approach that
enables this immersive image editing experience through personalized subject
swapping in existing images. Photoswap first learns the visual concept of the
subject from reference images and then swaps it into the target image using
pre-trained diffusion models in a training-free manner. We establish that a
well-conceptualized visual subject can be seamlessly transferred to any image
with appropriate self-attention and cross-attention manipulation, maintaining
the pose of the swapped subject and the overall coherence of the image.
Comprehensive experiments underscore the efficacy and controllability of
Photoswap in personalized subject swapping. Furthermore, Photoswap
significantly outperforms baseline methods in human ratings across subject
swapping, background preservation, and overall quality, revealing its vast
application potential, from entertainment to professional editing.
|
[
"cs.CV",
"cs.AI"
] | true |
2305.18445
|
2023-05-29T03:38:09Z
|
Intelligent gradient amplification for deep neural networks
|
[
"Sunitha Basodi",
"Krishna Pusuluri",
"Xueli Xiao",
"Yi Pan"
] |
Deep learning models offer superior performance compared to other machine
learning techniques for a variety of tasks and domains, but pose their own
challenges. In particular, deep learning models require larger training times
as the depth of a model increases, and suffer from vanishing gradients. Several
solutions address these problems independently, but there have been minimal
efforts to identify an integrated solution that improves the performance of a
model by addressing vanishing gradients, as well as accelerates the training
process to achieve higher performance at larger learning rates. In this work,
we intelligently determine which layers of a deep learning model to apply
gradient amplification to, using a formulated approach that analyzes gradient
fluctuations of layers during training. Detailed experiments are performed for
simpler and deeper neural networks using two different intelligent measures and
two different thresholds that determine the amplification layers, and a
training strategy where gradients are amplified only during certain epochs.
Results show that our amplification offers better performance compared to the
original models, and achieves accuracy improvement of around 2.5% on CIFAR- 10
and around 4.5% on CIFAR-100 datasets, even when the models are trained with
higher learning rates.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.18452
|
2023-05-29T04:03:46Z
|
Generating Driving Scenes with Diffusion
|
[
"Ethan Pronovost",
"Kai Wang",
"Nick Roy"
] |
In this paper we describe a learned method of traffic scene generation
designed to simulate the output of the perception system of a self-driving car.
In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel
combination of diffusion and object detection to directly create realistic and
physically plausible arrangements of discrete bounding boxes for agents. We
show that our scene generation model is able to adapt to different regions in
the US, producing scenarios that capture the intricacies of each region.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.18480
|
2023-05-29T11:47:43Z
|
Human Body Shape Classification Based on a Single Image
|
[
"Cameron Trotter",
"Filipa Peleja",
"Dario Dotti",
"Alberto de Santos"
] |
There is high demand for online fashion recommender systems that incorporate
the needs of the consumer's body shape. As such, we present a methodology to
classify human body shape from a single image. This is achieved through the use
of instance segmentation and keypoint estimation models, trained only on
open-source benchmarking datasets. The system is capable of performing in noisy
environments owing to to robust background subtraction. The proposed
methodology does not require 3D body recreation as a result of classification
based on estimated keypoints, nor requires historical information about a user
to operate - calculating all required measurements at the point of use. We
evaluate our methodology both qualitatively against existing body shape
classifiers and quantitatively against a novel dataset of images, which we
provide for use to the community. The resultant body shape classification can
be utilised in a variety of downstream tasks, such as input to size and fit
recommendation or virtual try-on systems.
|
[
"cs.CV",
"cs.LG",
"I.4"
] | false |
2305.18482
|
2023-05-29T11:57:02Z
|
Fashion Object Detection for Tops & Bottoms
|
[
"Andreas Petridis",
"Mirela Popa",
"Filipa Peleja",
"Dario Dotti",
"Alberto de Santos"
] |
Fashion is one of the largest world's industries and computer vision
techniques have been becoming more popular in recent years, in particular, for
tasks such as object detection and apparel segmentation. Even with the rapid
growth in computer vision solutions, specifically for the fashion industry,
many problems are far for being resolved. Therefore, not at all times,
adjusting out-of-the-box pre-trained computer vision models will provide the
desired solution. In the present paper is proposed a pipeline that takes a
noisy image with a person and specifically detects the regions with garments
that are bottoms or tops. Our solution implements models that are capable of
finding human parts in an image e.g. full-body vs half-body, or no human is
found. Then, other models knowing that there's a human and its composition
(e.g. not always we have a full-body) finds the bounding boxes/regions of the
image that very likely correspond to a bottom or a top. For the creation of
bounding boxes/regions task, a benchmark dataset was specifically prepared. The
results show that the Mask RCNN solution is robust, and generalized enough to
be used and scalable in unseen apparel/fashion data.
|
[
"cs.CV",
"cs.LG",
"I.4"
] | false |
2305.18563
|
2023-05-29T18:51:55Z
|
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired
Continual Learning
|
[
"Mustafa Burak Gurbuz",
"Jean Michael Moorman",
"Constantine Dovrolis"
] |
Deep neural networks (DNNs) struggle to learn in dynamic environments since
they rely on fixed datasets or stationary environments. Continual learning (CL)
aims to address this limitation and enable DNNs to accumulate knowledge
incrementally, similar to human learning. Inspired by how our brain
consolidates memories, a powerful strategy in CL is replay, which involves
training the DNN on a mixture of new and all seen classes. However, existing
replay methods overlook two crucial aspects of biological replay: 1) the brain
replays processed neural patterns instead of raw input, and 2) it prioritizes
the replay of recently learned information rather than revisiting all past
experiences. To address these differences, we propose SHARP, an efficient
neuro-inspired CL method that leverages sparse dynamic connectivity and
activation replay. Unlike other activation replay methods, which assume layers
not subjected to replay have been pretrained and fixed, SHARP can continually
update all layers. Also, SHARP is unique in that it only needs to replay few
recently seen classes instead of all past classes. Our experiments on five
datasets demonstrate that SHARP outperforms state-of-the-art replay methods in
class incremental learning. Furthermore, we showcase SHARP's flexibility in a
novel CL scenario where the boundaries between learning episodes are blurry.
The SHARP code is available at
\url{https://github.com/BurakGurbuz97/SHARP-Continual-Learning}.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.18583
|
2023-05-29T19:56:47Z
|
Controllable Text-to-Image Generation with GPT-4
|
[
"Tianjun Zhang",
"Yi Zhang",
"Vibhav Vineet",
"Neel Joshi",
"Xin Wang"
] |
Current text-to-image generation models often struggle to follow textual
instructions, especially the ones requiring spatial reasoning. On the other
hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable
precision in generating code snippets for sketching out text inputs
graphically, e.g., via TikZ. In this work, we introduce Control-GPT to guide
the diffusion-based text-to-image pipelines with programmatic sketches
generated by GPT-4, enhancing their abilities for instruction following.
Control-GPT works by querying GPT-4 to write TikZ code, and the generated
sketches are used as references alongside the text instructions for diffusion
models (e.g., ControlNet) to generate photo-realistic images. One major
challenge to training our pipeline is the lack of a dataset containing aligned
text, images, and sketches. We address the issue by converting instance masks
in existing datasets into polygons to mimic the sketches used at test time. As
a result, Control-GPT greatly boosts the controllability of image generation.
It establishes a new state-of-art on the spatial arrangement and object
positioning generation and enhances users' control of object positions, sizes,
etc., nearly doubling the accuracy of prior models. Our work, as a first
attempt, shows the potential for employing LLMs to enhance the performance in
computer vision tasks.
|
[
"cs.CV",
"cs.AI"
] | true |
2305.18641
|
2023-05-29T22:29:03Z
|
Enhanced Chart Understanding in Vision and Language Task via Cross-modal
Pre-training on Plot Table Pairs
|
[
"Mingyang Zhou",
"Yi R. Fung",
"Long Chen",
"Christopher Thomas",
"Heng Ji",
"Shih-Fu Chang"
] |
Building cross-model intelligence that can understand charts and communicate
the salient information hidden behind them is an appealing challenge in the
vision and language(V+L) community. The capability to uncover the underlined
table data of chart figures is a critical key to automatic chart understanding.
We introduce ChartT5, a V+L model that learns how to interpret table
information from chart images via cross-modal pre-training on plot table pairs.
Specifically, we propose two novel pre-training objectives: Masked Header
Prediction (MHP) and Masked Value Prediction (MVP) to facilitate the model with
different skills to interpret the table information. We have conducted
extensive experiments on chart question answering and chart summarization to
verify the effectiveness of the proposed pre-training strategies. In
particular, on the ChartQA benchmark, our ChartT5 outperforms the
state-of-the-art non-pretraining methods by over 8% performance gains.
|
[
"cs.CL",
"cs.CV"
] | false |
2306.11734
|
2023-05-29T09:28:34Z
|
Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
|
[
"Qinglong Cao",
"Yuntian Chen",
"Chao Ma",
"Xiaokang Yang"
] |
Few-shot aerial image segmentation is a challenging task that involves
precisely parsing objects in query aerial images with limited annotated
support. Conventional matching methods without consideration of varying object
orientations can fail to activate same-category objects with different
orientations. Moreover, conventional algorithms can lead to false recognition
of lower-scored rotated semantic objects. In response to these challenges, the
authors propose a novel few-shot rotation-invariant aerial semantic
segmentation network (FRINet). FRINet matches each query feature
rotation-adaptively with orientation-varying yet category-consistent support
information. The segmentation predictions from different orientations are
supervised by the same label, and the backbones are pre-trained in the base
category to boost segmentation performance. Experimental results demonstrate
that FRINet achieves state-of-the-art performance in few-shot aerial semantic
segmentation benchmark.
|
[
"cs.CV",
"eess.IV"
] | false |
2305.17871
|
2023-05-29T03:24:02Z
|
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors
on CT Scans
|
[
"Zifan Chen",
"Jiazheng Li",
"Jie Zhao",
"Yiting Liu",
"Hongfeng Li",
"Bin Dong",
"Lei Tang",
"Li Zhang"
] |
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal
for diagnosis and treatment. The challenges lie in the irregular shapes,
blurred boundaries of tumors, and the inefficiency of existing methods.
**Purpose:** We conducted a study to introduce a model, utilizing
human-guided knowledge and unique modules, to address the challenges of 3D
tumor segmentation.
**Methods:** We developed the PropNet framework, propagating radiologists'
knowledge from 2D annotations to the entire 3D space. This model consists of a
proposing stage for coarse segmentation and a refining stage for improved
segmentation, using two-way branches for enhanced performance and an up-down
strategy for efficiency.
**Results:** With 98 patient scans for training and 30 for validation, our
method achieves a significant agreement with manual annotation (Dice of 0.803)
and improves efficiency. The performance is comparable in different scenarios
and with various radiologists' annotations (Dice between 0.785 and 0.803).
Moreover, the model shows improved prognostic prediction performance (C-index
of 0.620 vs. 0.576) on an independent validation set of 42 patients with
advanced gastric cancer.
**Conclusions:** Our model generates accurate tumor segmentation efficiently
and stably, improving prognostic performance and reducing high-throughput image
reading workload. This model can accelerate the quantitative analysis of
gastric tumors and enhance downstream task performance.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.17911
|
2023-05-29T06:43:37Z
|
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in
Singapore
|
[
"Nirmalendu Prakash",
"Ming Shan Hee",
"Roy Ka-Wei Lee"
] |
Total Defence is a defence policy combining and extending the concept of
military defence and civil defence. While several countries have adopted total
defence as their defence policy, very few studies have investigated its
effectiveness. With the rapid proliferation of social media and digitalisation,
many social studies have been focused on investigating policy effectiveness
through specially curated surveys and questionnaires either through digital
media or traditional forms. However, such references may not truly reflect the
underlying sentiments about the target policies or initiatives of interest.
People are more likely to express their sentiment using communication mediums
such as starting topic thread on forums or sharing memes on social media. Using
Singapore as a case reference, this study aims to address this research gap by
proposing TotalDefMeme, a large-scale multi-modal and multi-attribute meme
dataset that captures public sentiments toward Singapore's Total Defence
policy. Besides supporting social informatics and public policy analysis of the
Total Defence policy, TotalDefMeme can also support many downstream multi-modal
machine learning tasks, such as aspect-based stance classification and
multi-modal meme clustering. We perform baseline machine learning experiments
on TotalDefMeme and evaluate its technical validity, and present possible
future interdisciplinary research directions and application scenarios using
the dataset as a baseline.
|
[
"cs.SI",
"cs.AI",
"cs.CL",
"cs.CV",
"I.2.7"
] | false |
2305.17929
|
2023-05-29T07:44:19Z
|
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of
Possibly Glossy Objects
|
[
"Yue Fan",
"Ivan Skorokhodov",
"Oleg Voynov",
"Savva Ignatyev",
"Evgeny Burnaev",
"Peter Wonka",
"Yiqun Wang"
] |
We develop a method that recovers the surface, materials, and illumination of
a scene from its posed multi-view images. In contrast to prior work, it does
not require any additional data and can handle glossy objects or bright
lighting. It is a progressive inverse rendering approach, which consists of
three stages. First, we reconstruct the scene radiance and signed distance
function (SDF) with our novel regularization strategy for specular reflections.
Our approach considers both the diffuse and specular colors, which allows for
handling complex view-dependent lighting effects for surface reconstruction.
Second, we distill light visibility and indirect illumination from the learned
SDF and radiance field using learnable mapping functions. Third, we design a
method for estimating the ratio of incoming direct light represented via
Spherical Gaussians reflected in a specular manner and then reconstruct the
materials and direct illumination of the scene. Experimental results
demonstrate that the proposed method outperforms the current state-of-the-art
in recovering surfaces, materials, and lighting without relying on any
additional data.
|
[
"cs.CV",
"cs.AI",
"cs.GR"
] | false |
2305.18260
|
2023-05-29T17:29:02Z
|
Synfeal: A Data-Driven Simulator for End-to-End Camera Localization
|
[
"Daniel Coelho",
"Miguel Oliveira",
"Paulo Dias"
] |
Collecting real-world data is often considered the bottleneck of Artificial
Intelligence, stalling the research progress in several fields, one of which is
camera localization. End-to-end camera localization methods are still
outperformed by traditional methods, and we argue that the inconsistencies
associated with the data collection techniques are restraining the potential of
end-to-end methods. Inspired by the recent data-centric paradigm, we propose a
framework that synthesizes large localization datasets based on realistic 3D
reconstructions of the real world. Our framework, termed Synfeal: Synthetic
from Real, is an open-source, data-driven simulator that synthesizes RGB images
by moving a virtual camera through a realistic 3D textured mesh, while
collecting the corresponding ground-truth camera poses. The results validate
that the training of camera localization algorithms on datasets generated by
Synfeal leads to better results when compared to datasets generated by
state-of-the-art methods. Using Synfeal, we conducted the first analysis of the
relationship between the size of the dataset and the performance of camera
localization algorithms. Results show that the performance significantly
increases with the dataset size. Our results also suggest that when a large
localization dataset with high quality is available, training from scratch
leads to better performances. Synfeal is publicly available at
https://github.com/DanielCoelho112/synfeal.
|
[
"cs.CV",
"cs.AI",
"cs.RO"
] | false |
2305.18439
|
2023-05-29T01:35:37Z
|
Alteration-free and Model-agnostic Origin Attribution of Generated
Images
|
[
"Zhenting Wang",
"Chen Chen",
"Yi Zeng",
"Lingjuan Lyu",
"Shiqing Ma"
] |
Recently, there has been a growing attention in image generation models.
However, concerns have emerged regarding potential misuse and intellectual
property (IP) infringement associated with these models. Therefore, it is
necessary to analyze the origin of images by inferring if a specific image was
generated by a particular model, i.e., origin attribution. Existing methods are
limited in their applicability to specific types of generative models and
require additional steps during training or generation. This restricts their
use with pre-trained models that lack these specific operations and may
compromise the quality of image generation. To overcome this problem, we first
develop an alteration-free and model-agnostic origin attribution method via
input reverse-engineering on image generation models, i.e., inverting the input
of a particular model for a specific image. Given a particular model, we first
analyze the differences in the hardness of reverse-engineering tasks for the
generated images of the given model and other images. Based on our analysis, we
propose a method that utilizes the reconstruction loss of reverse-engineering
to infer the origin. Our proposed method effectively distinguishes between
generated images from a specific generative model and other images, including
those generated by different models and real images.
|
[
"cs.CV",
"cs.CR",
"cs.LG"
] | false |
2305.18470
|
2023-05-29T09:16:07Z
|
Aligning Optimization Trajectories with Diffusion Models for Constrained
Design Generation
|
[
"Giorgio Giannone",
"Akash Srivastava",
"Ole Winther",
"Faez Ahmed"
] |
Generative models have had a profound impact on vision and language, paving
the way for a new era of multimodal generative applications. While these
successes have inspired researchers to explore using generative models in
science and engineering to accelerate the design process and reduce the
reliance on iterative optimization, challenges remain. Specifically,
engineering optimization methods based on physics still outperform generative
models when dealing with constrained environments where data is scarce and
precision is paramount. To address these challenges, we introduce Diffusion
Optimization Models (DOM) and Trajectory Alignment (TA), a learning framework
that demonstrates the efficacy of aligning the sampling trajectory of diffusion
models with the optimization trajectory derived from traditional physics-based
methods. This alignment ensures that the sampling process remains grounded in
the underlying physical principles. Our method allows for generating feasible
and high-performance designs in as few as two steps without the need for
expensive preprocessing, external surrogate models, or additional labeled data.
We apply our framework to structural topology optimization, a fundamental
problem in mechanical design, evaluating its performance on in- and
out-of-distribution configurations. Our results demonstrate that TA outperforms
state-of-the-art deep generative models on in-distribution configurations and
halves the inference computational cost. When coupled with a few steps of
optimization, it also improves manufacturability for out-of-distribution
conditions. By significantly improving performance and inference efficiency,
DOM enables us to generate high-quality designs in just a few steps and guide
them toward regions of high performance and manufacturability, paving the way
for the widespread application of generative models in large-scale data-driven
design.
|
[
"cs.LG",
"cs.CE",
"cs.CV"
] | false |
2305.18479
|
2023-05-29T11:17:51Z
|
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action
Recognition
|
[
"Petros Toupas",
"Christos-Savvas Bouganis",
"Dimitrios Tzovaras"
] |
3D Convolutional Neural Networks are gaining increasing attention from
researchers and practitioners and have found applications in many domains, such
as surveillance systems, autonomous vehicles, human monitoring systems, and
video retrieval. However, their widespread adoption is hindered by their high
computational and memory requirements, especially when resource-constrained
systems are targeted. This paper addresses the problem of mapping X3D, a
state-of-the-art model in Human Action Recognition that achieves accuracy of
95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow
generates an optimised stream-based hardware system, taking into account the
available resources and off-chip memory characteristics of the FPGA device. The
generated designs push further the current performance-accuracy pareto front,
and enable for the first time the targeting of such complex model architectures
for the Human Action Recognition task.
|
[
"cs.CV",
"cs.AI",
"cs.AR",
"cs.LG"
] | false |
2305.18487
|
2023-05-29T12:38:12Z
|
Solar Irradiance Anticipative Transformer
|
[
"Thomas M. Mercier",
"Tasmiat Rahman",
"Amin Sabet"
] |
This paper proposes an anticipative transformer-based model for short-term
solar irradiance forecasting. Given a sequence of sky images, our proposed
vision transformer encodes features of consecutive images, feeding into a
transformer decoder to predict irradiance values associated with future unseen
sky images. We show that our model effectively learns to attend only to
relevant features in images in order to forecast irradiance. Moreover, the
proposed anticipative transformer captures long-range dependencies between sky
images to achieve a forecasting skill of 21.45 % on a 15 minute ahead
prediction for a newly introduced dataset of all-sky images when compared to a
smart persistence model.
|
[
"cs.CV",
"cs.LG",
"physics.ao-ph"
] | false |
2305.18489
|
2023-05-29T13:14:05Z
|
A Transfer Learning and Explainable Solution to Detect mpox from
Smartphones images
|
[
"Mattia Giovanni Campana",
"Marco Colussi",
"Franca Delmastro",
"Sergio Mascetti",
"Elena Pagani"
] |
In recent months, the monkeypox (mpox) virus -- previously endemic in a
limited area of the world -- has started spreading in multiple countries until
being declared a ``public health emergency of international concern'' by the
World Health Organization. The alert was renewed in February 2023 due to a
persisting sustained incidence of the virus in several countries and worries
about possible new outbreaks. Low-income countries with inadequate
infrastructures for vaccine and testing administration are particularly at
risk.
A symptom of mpox infection is the appearance of skin rashes and eruptions,
which can drive people to seek medical advice. A technology that might help
perform a preliminary screening based on the aspect of skin lesions is the use
of Machine Learning for image classification. However, to make this technology
suitable on a large scale, it should be usable directly on mobile devices of
people, with a possible notification to a remote medical expert.
In this work, we investigate the adoption of Deep Learning to detect mpox
from skin lesion images. The proposal leverages Transfer Learning to cope with
the scarce availability of mpox image datasets. As a first step, a homogenous,
unpolluted, dataset is produced by manual selection and preprocessing of
available image data. It will also be released publicly to researchers in the
field. Then, a thorough comparison is conducted amongst several Convolutional
Neural Networks, based on a 10-fold stratified cross-validation. The best
models are then optimized through quantization for use on mobile devices;
measures of classification quality, memory footprint, and processing times
validate the feasibility of our proposal. Additionally, the use of eXplainable
AI is investigated as a suitable instrument to both technically and clinically
validate classification outcomes.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.18510
|
2023-05-29T16:14:56Z
|
RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban
Environments
|
[
"Daniel Coelho",
"Miguel Oliveira",
"Vitor Santos"
] |
Current approaches of Reinforcement Learning (RL) applied in urban Autonomous
Driving (AD) focus on decoupling the perception training from the driving
policy training. The main reason is to avoid training a convolution encoder
alongside a policy network, which is known to have issues related to sample
efficiency, degenerated feature representations, and catastrophic
self-overfitting. However, this paradigm can lead to representations of the
environment that are not aligned with the downstream task, which may result in
suboptimal performances. To address this limitation, this paper proposes RLAD,
the first Reinforcement Learning from Pixels (RLfP) method applied in the urban
AD domain. We propose several techniques to enhance the performance of an RLfP
algorithm in this domain, including: i) an image encoder that leverages both
image augmentations and Adaptive Local Signal Mixing (A-LIX) layers; ii)
WayConv1D, which is a waypoint encoder that harnesses the 2D geometrical
information of the waypoints using 1D convolutions; and iii) an auxiliary loss
to increase the significance of the traffic lights in the latent representation
of the environment. Experimental results show that RLAD significantly
outperforms all state-of-the-art RLfP methods on the NoCrash benchmark. We also
present an infraction analysis on the NoCrash-regular benchmark, which
indicates that RLAD performs better than all other methods in terms of both
collision rate and red light infractions.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.18512
|
2023-05-29T17:09:26Z
|
A Rainbow in Deep Network Black Boxes
|
[
"Florentin Guth",
"Brice Ménard",
"Gaspar Rochette",
"Stéphane Mallat"
] |
We introduce rainbow networks as a probabilistic model of trained deep neural
networks. The model cascades random feature maps whose weight distributions are
learned. It assumes that dependencies between weights at different layers are
reduced to rotations which align the input activations. Neuron weights within a
layer are independent after this alignment. Their activations define kernels
which become deterministic in the infinite-width limit. This is verified
numerically for ResNets trained on the ImageNet dataset. We also show that the
learned weight distributions have low-rank covariances. Rainbow networks thus
alternate between linear dimension reductions and non-linear high-dimensional
embeddings with white random features. Gaussian rainbow networks are defined
with Gaussian weight distributions. These models are validated numerically on
image classification on the CIFAR-10 dataset, with wavelet scattering networks.
We further show that during training, SGD updates the weight covariances while
mostly preserving the Gaussian initialization.
|
[
"cs.LG",
"cs.CV",
"eess.SP"
] | false |
2305.18565
|
2023-05-29T18:58:38Z
|
PaLI-X: On Scaling up a Multilingual Vision and Language Model
|
[
"Xi Chen",
"Josip Djolonga",
"Piotr Padlewski",
"Basil Mustafa",
"Soravit Changpinyo",
"Jialin Wu",
"Carlos Riquelme Ruiz",
"Sebastian Goodman",
"Xiao Wang",
"Yi Tay",
"Siamak Shakeri",
"Mostafa Dehghani",
"Daniel Salz",
"Mario Lucic",
"Michael Tschannen",
"Arsha Nagrani",
"Hexiang Hu",
"Mandar Joshi",
"Bo Pang",
"Ceslee Montgomery",
"Paulina Pietrzyk",
"Marvin Ritter",
"AJ Piergiovanni",
"Matthias Minderer",
"Filip Pavetic",
"Austin Waters",
"Gang Li",
"Ibrahim Alabdulmohsin",
"Lucas Beyer",
"Julien Amelot",
"Kenton Lee",
"Andreas Peter Steiner",
"Yang Li",
"Daniel Keysers",
"Anurag Arnab",
"Yuanzhong Xu",
"Keran Rong",
"Alexander Kolesnikov",
"Mojtaba Seyedhosseini",
"Anelia Angelova",
"Xiaohua Zhai",
"Neil Houlsby",
"Radu Soricut"
] |
We present the training recipe and results of scaling up PaLI-X, a
multilingual vision and language model, both in terms of size of the components
and the breadth of its training task mixture. Our model achieves new levels of
performance on a wide-range of varied and complex tasks, including multiple
image-based captioning and question-answering tasks, image-based document
understanding and few-shot (in-context) learning, as well as object detection,
video question answering, and video captioning. PaLI-X advances the
state-of-the-art on most vision-and-language benchmarks considered (25+ of
them). Finally, we observe emerging capabilities, such as complex counting and
multilingual object detection, tasks that are not explicitly in the training
mix.
|
[
"cs.CV",
"cs.CL",
"cs.LG"
] | true |
2305.17854
|
2023-05-29T02:36:16Z
|
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
|
[
"Zhen Zhang",
"Mengting Hu",
"Shiwan Zhao",
"Minlie Huang",
"Haotian Wang",
"Lemao Liu",
"Zhirui Zhang",
"Zhe Liu",
"Bingzhe Wu"
] |
Most named entity recognition (NER) systems focus on improving model
performance, ignoring the need to quantify model uncertainty, which is critical
to the reliability of NER systems in open environments. Evidential deep
learning (EDL) has recently been proposed as a promising solution to explicitly
model predictive uncertainty for classification tasks. However, directly
applying EDL to NER applications faces two challenges, i.e., the problems of
sparse entities and OOV/OOD entities in NER tasks. To address these challenges,
we propose a trustworthy NER framework named E-NER by introducing two
uncertainty-guided loss terms to the conventional EDL, along with a series of
uncertainty-guided training strategies. Experiments show that E-NER can be
applied to multiple NER paradigms to obtain accurate uncertainty estimation.
Furthermore, compared to state-of-the-art baselines, the proposed method
achieves a better OOV/OOD detection performance and better generalization
ability on OOV entities.
|
[
"cs.CL"
] | false |
2305.17855
|
2023-05-29T02:37:37Z
|
Vec2Gloss: definition modeling leveraging contextualized vectors with
Wordnet gloss
|
[
"Yu-Hsiang Tseng",
"Mao-Chang Ku",
"Wei-Ling Chen",
"Yu-Lin Chang",
"Shu-Kai Hsieh"
] |
Contextualized embeddings are proven to be powerful tools in multiple NLP
tasks. Nonetheless, challenges regarding their interpretability and capability
to represent lexical semantics still remain. In this paper, we propose that the
task of definition modeling, which aims to generate the human-readable
definition of the word, provides a route to evaluate or understand the high
dimensional semantic vectors. We propose a `Vec2Gloss' model, which produces
the gloss from the target word's contextualized embeddings. The generated
glosses of this study are made possible by the systematic gloss patterns
provided by Chinese Wordnet. We devise two dependency indices to measure the
semantic and contextual dependency, which are used to analyze the generated
texts in gloss and token levels. Our results indicate that the proposed
`Vec2Gloss' model opens a new perspective to the lexical-semantic applications
of contextualized embeddings.
|
[
"cs.CL"
] | false |
2305.17888
|
2023-05-29T05:22:11Z
|
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
|
[
"Zechun Liu",
"Barlas Oguz",
"Changsheng Zhao",
"Ernie Chang",
"Pierre Stock",
"Yashar Mehdad",
"Yangyang Shi",
"Raghuraman Krishnamoorthi",
"Vikas Chandra"
] |
Several post-training quantization methods have been applied to large
language models (LLMs), and have been shown to perform well down to 8-bits. We
find that these methods break down at lower bit precision, and investigate
quantization aware training for LLMs (LLM-QAT) to push quantization levels even
further. We propose a data-free distillation method that leverages generations
produced by the pre-trained model, which better preserves the original output
distribution and allows quantizing any generative model independent of its
training data, similar to post-training quantization methods. In addition to
quantizing weights and activations, we also quantize the KV cache, which is
critical for increasing throughput and support long sequence dependencies at
current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B,
at quantization levels down to 4-bits. We observe large improvements over
training-free methods, especially in the low-bit settings.
|
[
"cs.CL"
] | false |
2305.17906
|
2023-05-29T06:35:40Z
|
Byte-Level Grammatical Error Correction Using Synthetic and Curated
Corpora
|
[
"Svanhvít Lilja Ingólfsdóttir",
"Pétur Orri Ragnarsson",
"Haukur Páll Jónsson",
"Haukur Barri Símonarson",
"Vilhjálmur Þorsteinsson",
"Vésteinn Snæbjarnarson"
] |
Grammatical error correction (GEC) is the task of correcting typos, spelling,
punctuation and grammatical issues in text. Approaching the problem as a
sequence-to-sequence task, we compare the use of a common subword unit
vocabulary and byte-level encoding. Initial synthetic training data is created
using an error-generating pipeline, and used for finetuning two subword-level
models and one byte-level model. Models are then finetuned further on
hand-corrected error corpora, including texts written by children, university
students, dyslexic and second-language writers, and evaluated over different
error types and origins. We show that a byte-level model enables higher
correction quality than a subword approach, not only for simple spelling
errors, but also for more complex semantic, stylistic and grammatical issues.
In particular, initial training on synthetic corpora followed by finetuning on
a relatively small parallel corpus of real-world errors helps the byte-level
model correct a wide range of commonly occurring errors. Our experiments are
run for the Icelandic language but should hold for other similar languages,
particularly morphologically rich ones.
|
[
"cs.CL"
] | false |
2305.17968
|
2023-05-29T09:20:34Z
|
Data Augmentation for Low-Resource Keyphrase Generation
|
[
"Krishna Garg",
"Jishnu Ray Chowdhury",
"Cornelia Caragea"
] |
Keyphrase generation is the task of summarizing the contents of any given
article into a few salient phrases (or keyphrases). Existing works for the task
mostly rely on large-scale annotated datasets, which are not easy to acquire.
Very few works address the problem of keyphrase generation in low-resource
settings, but they still rely on a lot of additional unlabeled data for
pretraining and on automatic methods for pseudo-annotations. In this paper, we
present data augmentation strategies specifically to address keyphrase
generation in purely resource-constrained domains. We design techniques that
use the full text of the articles to improve both present and absent keyphrase
generation. We test our approach comprehensively on three datasets and show
that the data augmentation strategies consistently improve the state-of-the-art
performance. We release our source code at
https://github.com/kgarg8/kpgen-lowres-data-aug.
|
[
"cs.CL"
] | false |
2305.18023
|
2023-05-29T11:28:26Z
|
Abstractive Summarization as Augmentation for Document-Level Event
Detection
|
[
"Janko Vidaković",
"Filip Karlo Došilović",
"Domagoj Pluščec"
] |
Transformer-based models have consistently produced substantial performance
gains across a variety of NLP tasks, compared to shallow models. However, deep
models are orders of magnitude more computationally expensive than shallow
models, especially on tasks with large sequence lengths, such as document-level
event detection. In this work, we attempt to bridge the performance gap between
shallow and deep models on document-level event detection by using abstractive
text summarization as an augmentation method. We augment the DocEE dataset by
generating abstractive summaries of examples from low-resource classes. For
classification, we use linear SVM with TF-IDF representations and RoBERTa-base.
We use BART for zero-shot abstractive summarization, making our augmentation
setup less resource-intensive compared to supervised fine-tuning. We experiment
with four decoding methods for text generation, namely beam search, top-k
sampling, top-p sampling, and contrastive search. Furthermore, we investigate
the impact of using document titles as additional input for classification. Our
results show that using the document title offers 2.04% and 3.19% absolute
improvement in macro F1-score for linear SVM and RoBERTa, respectively.
Augmentation via summarization further improves the performance of linear SVM
by about 0.5%, varying slightly across decoding methods. Overall, our
augmentation setup yields insufficient improvements for linear SVM compared to
RoBERTa.
|
[
"cs.CL"
] | false |
2305.18152
|
2023-05-29T15:29:49Z
|
Extrinsic Factors Affecting the Accuracy of Biomedical NER
|
[
"Zhiyi Li",
"Shengjie Zhang",
"Yujie Song",
"Jungyeul Park"
] |
Biomedical named entity recognition (NER) is a critial task that aims to
identify structured information in clinical text, which is often replete with
complex, technical terms and a high degree of variability. Accurate and
reliable NER can facilitate the extraction and analysis of important biomedical
information, which can be used to improve downstream applications including the
healthcare system. However, NER in the biomedical domain is challenging due to
limited data availability, as the high expertise, time, and expenses are
required to annotate its data. In this paper, by using the limited data, we
explore various extrinsic factors including the corpus annotation scheme, data
augmentation techniques, semi-supervised learning and Brill transformation, to
improve the performance of a NER model on a clinical text dataset (i2b2 2012,
\citet{sun-rumshisky-uzuner:2013}). Our experiments demonstrate that these
approaches can significantly improve the model's F1 score from original 73.74
to 77.55. Our findings suggest that considering different extrinsic factors and
combining these techniques is a promising approach for improving NER
performance in the biomedical domain where the size of data is limited.
|
[
"cs.CL"
] | false |
2305.18200
|
2023-05-29T16:54:10Z
|
Contextual Knowledge Learning For Dialogue Generation
|
[
"Wen Zheng",
"Natasa Milic-Frayling",
"Ke Zhou"
] |
Incorporating conversational context and knowledge into dialogue generation
models has been essential for improving the quality of the generated responses.
The context, comprising utterances from previous dialogue exchanges, is used as
a source of content for response generation and as a means of selecting
external knowledge. However, to avoid introducing irrelevant content, it is key
to enable fine-grained scoring of context and knowledge. In this paper, we
present a novel approach to context and knowledge weighting as an integral part
of model training. We guide the model training through a Contextual Knowledge
Learning (CKL) process which involves Latent Vectors for context and knowledge,
respectively. CKL Latent Vectors capture the relationship between context,
knowledge, and responses through weak supervision and enable differential
weighting of context utterances and knowledge sentences during the training
process. Experiments with two standard datasets and human evaluation
demonstrate that CKL leads to a significant improvement compared with the
performance of six strong baseline models and shows robustness with regard to
reduced sizes of training sets.
|
[
"cs.CL"
] | false |
2305.18201
|
2023-05-29T16:54:24Z
|
A Critical Evaluation of Evaluations for Long-form Question Answering
|
[
"Fangyuan Xu",
"Yixiao Song",
"Mohit Iyyer",
"Eunsol Choi"
] |
Long-form question answering (LFQA) enables answering a wide range of
questions, but its flexibility poses enormous challenges for evaluation. We
perform the first targeted study of the evaluation of long-form answers,
covering both human and automatic evaluation practices. We hire domain experts
in seven areas to provide preference judgments over pairs of answers, along
with free-form justifications for their choices. We present a careful analysis
of experts' evaluation, which focuses on new aspects such as the
comprehensiveness of the answer. Next, we examine automatic text generation
metrics, finding that no existing metrics are predictive of human preference
judgments. However, some metrics correlate with fine-grained aspects of answers
(e.g., coherence). We encourage future work to move away from a single "overall
score" of the answer and adopt a multi-faceted evaluation, targeting aspects
such as factuality and completeness. We publicly release all of our annotations
and code to spur future work into LFQA evaluation.
|
[
"cs.CL"
] | false |
2305.18294
|
2023-05-29T17:59:15Z
|
Transformer Language Models Handle Word Frequency in Prediction Head
|
[
"Goro Kobayashi",
"Tatsuki Kuribayashi",
"Sho Yokoi",
"Kentaro Inui"
] |
Prediction head is a crucial component of Transformer language models.
Despite its direct impact on prediction, this component has often been
overlooked in analyzing Transformers. In this study, we investigate the inner
workings of the prediction head, specifically focusing on bias parameters. Our
experiments with BERT and GPT-2 models reveal that the biases in their word
prediction heads play a significant role in the models' ability to reflect word
frequency in a corpus, aligning with the logit adjustment method commonly used
in long-tailed learning. We also quantify the effect of controlling the biases
in practical auto-regressive text generation scenarios; under a particular
setting, more diverse text can be generated without compromising text quality.
|
[
"cs.CL"
] | false |
2305.18513
|
2023-05-29T17:50:52Z
|
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using
Training Dynamics
|
[
"Arash Ardakani",
"Altan Haan",
"Shangyin Tan",
"Doru Thom Popovici",
"Alvin Cheung",
"Costin Iancu",
"Koushik Sen"
] |
Transformer-based models, such as BERT and ViT, have achieved
state-of-the-art results across different natural language processing (NLP) and
computer vision (CV) tasks. However, these models are extremely memory
intensive during their fine-tuning process, making them difficult to deploy on
GPUs with limited memory resources. To address this issue, we introduce a new
tool called SlimFit that reduces the memory requirements of these models by
dynamically analyzing their training dynamics and freezing less-contributory
layers during fine-tuning. The layers to freeze are chosen using a runtime
inter-layer scheduling algorithm. SlimFit adopts quantization and pruning for
particular layers to balance the load of dynamic activations and to minimize
the memory footprint of static activations, where static activations refer to
those that cannot be discarded regardless of freezing. This allows SlimFit to
freeze up to 95% of layers and reduce the overall on-device GPU memory usage of
transformer-based models such as ViT and BERT by an average of 2.2x, across
different NLP and CV benchmarks/datasets such as GLUE, SQuAD 2.0, CIFAR-10,
CIFAR-100 and ImageNet with an average degradation of 0.2% in accuracy. For
such NLP and CV tasks, SlimFit can reduce up to 3.1x the total on-device memory
usage with an accuracy degradation of only up to 0.4%. As a result, while
fine-tuning of ViT on ImageNet and BERT on SQuAD 2.0 with a batch size of 128
requires 3 and 2 32GB GPUs respectively, SlimFit enables their fine-tuning on a
single 32GB GPU without any significant accuracy degradation.
|
[
"cs.CL"
] | false |
2305.18576
|
2023-05-29T19:37:26Z
|
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding
|
[
"Zichen Liu",
"Xuyuan Liu",
"Yanlong Wen",
"Guoqing Zhao",
"Fen Xia",
"Xiaojie Yuan"
] |
ICD coding is designed to assign the disease codes to electronic health
records (EHRs) upon discharge, which is crucial for billing and clinical
statistics. In an attempt to improve the effectiveness and efficiency of manual
coding, many methods have been proposed to automatically predict ICD codes from
clinical notes. However, most previous works ignore the decisive information
contained in structured medical data in EHRs, which is hard to be captured from
the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal
Attention Network (TreeMAN) to fuse tabular features and textual features into
multimodal representations by enhancing the text representations with
tree-based features via the attention mechanism. Tree-based features are
constructed according to decision trees learned from structured multimodal
medical data, which capture the decisive information about ICD coding. We can
apply the same multi-label classifier from previous text models to the
multimodal representations to predict ICD codes. Experiments on two MIMIC
datasets show that our method outperforms prior state-of-the-art ICD coding
approaches. The code is available at https://github.com/liu-zichen/TreeMAN.
|
[
"cs.CL",
"I.2.7"
] | false |
2305.18598
|
2023-05-29T20:30:38Z
|
A Method for Studying Semantic Construal in Grammatical Constructions
with Interpretable Contextual Embedding Spaces
|
[
"Gabriella Chronis",
"Kyle Mahowald",
"Katrin Erk"
] |
We study semantic construal in grammatical constructions using large language
models. First, we project contextual word embeddings into three interpretable
semantic spaces, each defined by a different set of psycholinguistic feature
norms. We validate these interpretable spaces and then use them to
automatically derive semantic characterizations of lexical items in two
grammatical constructions: nouns in subject or object position within the same
sentence, and the AANN construction (e.g., `a beautiful three days'). We show
that a word in subject position is interpreted as more agentive than the very
same word in object position, and that the nouns in the AANN construction are
interpreted as more measurement-like than when in the canonical alternation.
Our method can probe the distributional meaning of syntactic constructions at a
templatic level, abstracted away from specific lexemes.
|
[
"cs.CL"
] | false |
2305.18638
|
2023-05-29T22:05:29Z
|
Short Answer Grading Using One-shot Prompting and Text Similarity
Scoring Model
|
[
"Su-Youn Yoon"
] |
In this study, we developed an automated short answer grading (ASAG) model
that provided both analytic scores and final holistic scores. Short answer
items typically consist of multiple sub-questions, and providing an analytic
score and the text span relevant to each sub-question can increase the
interpretability of the automated scores. Furthermore, they can be used to
generate actionable feedback for students. Despite these advantages, most
studies have focused on predicting only holistic scores due to the difficulty
in constructing dataset with manual annotations. To address this difficulty, we
used large language model (LLM)-based one-shot prompting and a text similarity
scoring model with domain adaptation using small manually annotated dataset.
The accuracy and quadratic weighted kappa of our model were 0.67 and 0.71 on a
subset of the publicly available ASAG dataset. The model achieved a substantial
improvement over the majority baseline.
|
[
"cs.CL",
"I.2.7"
] | false |
2306.04480
|
2023-05-29T12:36:56Z
|
Exploring the Compositional Generalization in Context Dependent
Text-to-SQL Parsing
|
[
"Aiwei Liu",
"Wei Liu",
"Xuming Hu",
"Shuang Li",
"Fukun Ma",
"Yawen Yang",
"Lijie Wen"
] |
In the context-dependent Text-to-SQL task, the generated SQL statements are
refined iteratively based on the user input utterance from each interaction.
The input text from each interaction can be viewed as component modifications
to the previous SQL statements, which could be further extracted as the
modification patterns. Since these modification patterns could also be combined
with other SQL statements, the models are supposed to have the compositional
generalization to these novel combinations. This work is the first exploration
of compositional generalization in context-dependent Text-to-SQL scenarios. To
facilitate related studies, we constructed two challenging benchmarks named
\textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification
patterns and existing SQL statements. The following experiments show that all
current models struggle on our proposed benchmarks. Furthermore, we found that
better aligning the previous SQL statements with the input utterance could give
models better compositional generalization ability. Based on these
observations, we propose a method named \texttt{p-align} to improve the
compositional generalization of Text-to-SQL models. Further experiments
validate the effectiveness of our method. Source code and data are available.
|
[
"cs.CL",
"68T50",
"I.2.7"
] | false |
2305.17951
|
2023-05-29T08:24:42Z
|
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER
|
[
"Amirhossein Layegh",
"Amir H. Payberah",
"Ahmet Soylu",
"Dumitru Roman",
"Mihhail Matskin"
] |
Prompt-based language models have produced encouraging results in numerous
applications, including Named Entity Recognition (NER) tasks. NER aims to
identify entities in a sentence and provide their types. However, the strong
performance of most available NER approaches is heavily dependent on the design
of discrete prompts and a verbalizer to map the model-predicted outputs to
entity categories, which are complicated undertakings. To address these
challenges, we present ContrastNER, a prompt-based NER framework that employs
both discrete and continuous tokens in prompts and uses a contrastive learning
approach to learn the continuous prompts and forecast entity types. The
experimental results demonstrate that ContrastNER obtains competitive
performance to the state-of-the-art NER methods in high-resource settings and
outperforms the state-of-the-art models in low-resource circumstances without
requiring extensive manual prompt engineering and verbalizer design.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.18099
|
2023-05-29T14:09:14Z
|
Writing user personas with Large Language Models: Testing phase 6 of a
Thematic Analysis of semi-structured interviews
|
[
"Stefano De Paoli"
] |
The goal of this paper is establishing if we can satisfactorily perform a
Thematic Analysis (TA) of semi-structured interviews using a Large Language
Model (more precisely GPT3.5-Turbo). Building on previous work by the author,
which established an embryonal process for conducting a TA with the model, this
paper will perform a further analysis and then cover the last phase of a TA
(phase 6), which entails the writing up of the result. This phase was not
covered by the previous work. In particular, the focus will be on using the
results of a TA done with the LLM on a dataset of user interviews, for writing
user personas, with the model building on the TA to produce the personas
narratives. User personas are models of real users, usually built from a data
analysis like interviews with a sample of users. User personas are tools often
used in User Centered Design processes. The paper shows that the model can
build basic user personas with an acceptable quality deriving them from themes,
and that the model can serve for the generation of ideas for user personas.
|
[
"cs.CL",
"cs.CY"
] | false |
2305.18109
|
2023-05-29T14:23:34Z
|
Medical Dialogue Generation via Dual Flow Modeling
|
[
"Kaishuai Xu",
"Wenjun Hou",
"Yi Cheng",
"Jian Wang",
"Wenjie Li"
] |
Medical dialogue systems (MDS) aim to provide patients with medical services,
such as diagnosis and prescription. Since most patients cannot precisely
describe their symptoms, dialogue understanding is challenging for MDS.
Previous studies mainly addressed this by extracting the mentioned medical
entities as critical dialogue history information. In this work, we argue that
it is also essential to capture the transitions of the medical entities and the
doctor's dialogue acts in each turn, as they help the understanding of how the
dialogue flows and enhance the prediction of the entities and dialogue acts to
be adopted in the following turn. Correspondingly, we propose a Dual Flow
enhanced Medical (DFMed) dialogue generation framework. It extracts the medical
entities and dialogue acts used in the dialogue history and models their
transitions with an entity-centric graph flow and a sequential act flow,
respectively. We employ two sequential models to encode them and devise an
interweaving component to enhance their interactions. Experiments on two
datasets demonstrate that our method exceeds baselines in both automatic and
manual evaluations.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.18156
|
2023-05-29T15:31:29Z
|
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A
Study on Performance and Controllability in Prompt-Based Methods
|
[
"Mengsay Loem",
"Masahiro Kaneko",
"Sho Takase",
"Naoaki Okazaki"
] |
Large-scale pre-trained language models such as GPT-3 have shown remarkable
performance across various natural language processing tasks. However, applying
prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks
and their controllability remains underexplored. Controllability in GEC is
crucial for real-world applications, particularly in educational settings,
where the ability to tailor feedback according to learner levels and specific
error types can significantly enhance the learning process. This paper
investigates the performance and controllability of prompt-based methods with
GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact
of task instructions and examples on GPT-3's output, focusing on controlling
aspects such as minimal edits, fluency edits, and learner levels. Our findings
demonstrate that GPT-3 could effectively perform GEC tasks, outperforming
existing supervised and unsupervised approaches. We also showed that GPT-3
could achieve controllability when appropriate task instructions and examples
are given.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.18585
|
2023-05-29T19:59:40Z
|
Exploiting Explainability to Design Adversarial Attacks and Evaluate
Attack Resilience in Hate-Speech Detection Models
|
[
"Pranath Reddy Kumbam",
"Sohaib Uddin Syed",
"Prashanth Thamminedi",
"Suhas Harish",
"Ian Perera",
"Bonnie J. Dorr"
] |
The advent of social media has given rise to numerous ethical challenges,
with hate speech among the most significant concerns. Researchers are
attempting to tackle this problem by leveraging hate-speech detection and
employing language models to automatically moderate content and promote civil
discourse. Unfortunately, recent studies have revealed that hate-speech
detection systems can be misled by adversarial attacks, raising concerns about
their resilience. While previous research has separately addressed the
robustness of these models under adversarial attacks and their
interpretability, there has been no comprehensive study exploring their
intersection. The novelty of our work lies in combining these two critical
aspects, leveraging interpretability to identify potential vulnerabilities and
enabling the design of targeted adversarial attacks. We present a comprehensive
and comparative analysis of adversarial robustness exhibited by various
hate-speech detection models. Our study evaluates the resilience of these
models against adversarial attacks using explainability techniques. To gain
insights into the models' decision-making processes, we employ the Local
Interpretable Model-agnostic Explanations (LIME) framework. Based on the
explainability results obtained by LIME, we devise and execute targeted attacks
on the text by leveraging the TextAttack tool. Our findings enhance the
understanding of the vulnerabilities and strengths exhibited by
state-of-the-art hate-speech detection models. This work underscores the
importance of incorporating explainability in the development and evaluation of
such models to enhance their resilience against adversarial attacks.
Ultimately, this work paves the way for creating more robust and reliable
hate-speech detection systems, fostering safer online environments and
promoting ethical discourse on social media platforms.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.18623
|
2023-05-29T21:16:42Z
|
Alfred: A System for Prompted Weak Supervision
|
[
"Peilin Yu",
"Stephen H. Bach"
] |
Alfred is the first system for programmatic weak supervision (PWS) that
creates training data for machine learning by prompting. In contrast to typical
PWS systems where weak supervision sources are programs coded by experts,
Alfred enables users to encode their subject matter expertise via natural
language prompts for language and vision-language models. Alfred provides a
simple Python interface for the key steps of this emerging paradigm, with a
high-throughput backend for large-scale data labeling. Users can quickly
create, evaluate, and refine their prompt-based weak supervision sources; map
the results to weak labels; and resolve their disagreements with a label model.
Alfred enables a seamless local development experience backed by models served
from self-managed computing clusters. It automatically optimizes the execution
of prompts with optimized batching mechanisms. We find that this optimization
improves query throughput by 2.9x versus a naive approach. We present two
example use cases demonstrating Alfred on YouTube comment spam detection and
pet breeds classification. Alfred is open source, available at
https://github.com/BatsResearch/alfred.
|
[
"cs.LG",
"cs.CL"
] | false |
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