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We evaluate machine learning methods for event classification in the
Active-Target Time Projection Chamber detector at the National Superconducting
Cyclotron Laboratory (NSCL) at Michigan State University. An automated method
to single out the desired reaction product would result in more accurate
physics results as well as a faster analysis process. Binary and multi-class
classification methods were tested on data produced by the $^{46}$Ar(p,p)
experiment run at the NSCL in September 2015. We found a Convolutional Neural
Network to be the most successful classifier of proton scattering events for
transfer learning. Results from this investigation and recommendations for
event classification in future experiments are presented. | [
"cs.CV",
"cs.LG",
"nucl-ex"
] |
Data selection methods, such as active learning and core-set selection, are
useful tools for improving the data efficiency of deep learning models on
large-scale datasets. However, recent deep learning models have moved forward
from independent and identically distributed data to graph-structured data,
such as social networks, e-commerce user-item graphs, and knowledge graphs.
This evolution has led to the emergence of Graph Neural Networks (GNNs) that go
beyond the models existing data selection methods are designed for. Therefore,
we present Grain, an efficient framework that opens up a new perspective
through connecting data selection in GNNs with social influence maximization.
By exploiting the common patterns of GNNs, Grain introduces a novel feature
propagation concept, a diversified influence maximization objective with novel
influence and diversity functions, and a greedy algorithm with an approximation
guarantee into a unified framework. Empirical studies on public datasets
demonstrate that Grain significantly improves both the performance and
efficiency of data selection (including active learning and core-set selection)
for GNNs. To the best of our knowledge, this is the first attempt to bridge two
largely parallel threads of research, data selection, and social influence
maximization, in the setting of GNNs, paving new ways for improving data
efficiency. | [
"cs.LG",
"cs.AI"
] |
We describe a method for estimating human head pose in a color image that
contains enough of information to locate the head silhouette and detect
non-trivial color edges of individual facial features. The method works by
spotting the human head on an arbitrary background, extracting the head
outline, and locating facial features necessary to describe the head
orientation in the 3D space. It is robust enough to work with both color and
gray-level images featuring quasi-frontal views of a human head under variable
lighting conditions. | [
"cs.CV"
] |
Posterior collapse in Variational Autoencoders (VAEs) arises when the
variational posterior distribution closely matches the prior for a subset of
latent variables. This paper presents a simple and intuitive explanation for
posterior collapse through the analysis of linear VAEs and their direct
correspondence with Probabilistic PCA (pPCA). We explain how posterior collapse
may occur in pPCA due to local maxima in the log marginal likelihood.
Unexpectedly, we prove that the ELBO objective for the linear VAE does not
introduce additional spurious local maxima relative to log marginal likelihood.
We show further that training a linear VAE with exact variational inference
recovers an identifiable global maximum corresponding to the principal
component directions. Empirically, we find that our linear analysis is
predictive even for high-capacity, non-linear VAEs and helps explain the
relationship between the observation noise, local maxima, and posterior
collapse in deep Gaussian VAEs. | [
"cs.LG",
"stat.ML"
] |
Recent work has shown deep learning can accelerate the prediction of physical
dynamics relative to numerical solvers. However, limited physical accuracy and
an inability to generalize under distributional shift limit its applicability
to the real world. We propose to improve accuracy and generalization by
incorporating symmetries into convolutional neural networks. Specifically, we
employ a variety of methods each tailored to enforce a different symmetry. Our
models are both theoretically and experimentally robust to distributional shift
by symmetry group transformations and enjoy favorable sample complexity. We
demonstrate the advantage of our approach on a variety of physical dynamics
including Rayleigh B\'enard convection and real-world ocean currents and
temperatures. Compared with image or text applications, our work is a
significant step towards applying equivariant neural networks to
high-dimensional systems with complex dynamics. We open-source our simulation,
data, and code at \url{https://github.com/Rose-STL-Lab/Equivariant-Net}. | [
"cs.LG",
"math.RT",
"stat.ML"
] |
Translating potential disease biomarkers between multi-species 'omics'
experiments is a new direction in biomedical research. The existing methods are
limited to simple experimental setups such as basic healthy-diseased
comparisons. Most of these methods also require an a priori matching of the
variables (e.g., genes or metabolites) between the species. However, many
experiments have a complicated multi-way experimental design often involving
irregularly-sampled time-series measurements, and for instance metabolites do
not always have known matchings between organisms. We introduce a Bayesian
modelling framework for translating between multiple species the results from
'omics' experiments having a complex multi-way, time-series experimental
design. The underlying assumption is that the unknown matching can be inferred
from the response of the variables to multiple covariates including time. | [
"stat.ML"
] |
Policy gradient (PG) algorithms have been widely used in reinforcement
learning (RL). However, PG algorithms rely on exploiting the value function
being learned with the first-order update locally, which results in limited
sample efficiency. In this work, we propose an alternative method called
Zeroth-Order Supervised Policy Improvement (ZOSPI). ZOSPI exploits the
estimated value function $Q$ globally while preserving the local exploitation
of the PG methods based on zeroth-order policy optimization. This learning
paradigm follows Q-learning but overcomes the difficulty of efficiently
operating argmax in continuous action space. It finds max-valued action within
a small number of samples. The policy learning of ZOSPI has two steps: First,
it samples actions and evaluates those actions with a learned value estimator,
and then it learns to perform the action with the highest value through
supervised learning. We further demonstrate such a supervised learning
framework can learn multi-modal policies. Experiments show that ZOSPI achieves
competitive results on the continuous control benchmarks with a remarkable
sample efficiency. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Inferring a decision tree from a given dataset is one of the classic problems
in machine learning. This problem consists of buildings, from a labelled
dataset, a tree such that each node corresponds to a class and a path between
the tree root and a leaf corresponds to a conjunction of features to be
satisfied in this class. Following the principle of parsimony, we want to infer
a minimal tree consistent with the dataset. Unfortunately, inferring an optimal
decision tree is known to be NP-complete for several definitions of optimality.
Hence, the majority of existing approaches relies on heuristics, and as for the
few exact inference approaches, they do not work on large data sets. In this
paper, we propose a novel approach for inferring a decision tree of a minimum
depth based on the incremental generation of Boolean formula. The experimental
results indicate that it scales sufficiently well and the time it takes to run
grows slowly with the size of dataset. | [
"cs.LG",
"stat.ML"
] |
In medical image segmentation, it is difficult to mark ambiguous areas
accurately with binary masks, especially when dealing with small lesions.
Therefore, it is a challenge for radiologists to reach a consensus by using
binary masks under the condition of multiple annotations. However, these areas
may contain anatomical structures that are conducive to diagnosis. Uncertainty
is introduced to study these situations. Nevertheless, the uncertainty is
usually measured by the variances between predictions in a multiple trial way.
It is not intuitive, and there is no exact correspondence in the image.
Inspired by image matting, we introduce matting as a soft segmentation method
and a new perspective to deal with and represent uncertain regions into medical
scenes, namely medical matting. More specifically, because there is no
available medical matting dataset, we first labeled two medical datasets with
alpha matte. Secondly, the matting method applied to the natural image is not
suitable for the medical scene, so we propose a new architecture to generate
binary masks and alpha matte in a row. Thirdly, the uncertainty map is
introduced to highlight the ambiguous regions from the binary results and
improve the matting performance. Evaluated on these datasets, the proposed
model outperformed state-of-the-art matting algorithms by a large margin, and
alpha matte is proved to be a more efficient labeling form than a binary mask. | [
"cs.CV"
] |
For embodied agents to infer representations of the underlying 3D physical
world they inhabit, they should efficiently combine multisensory cues from
numerous trials, e.g., by looking at and touching objects. Despite its
importance, multisensory 3D scene representation learning has received less
attention compared to the unimodal setting. In this paper, we propose the
Generative Multisensory Network (GMN) for learning latent representations of 3D
scenes which are partially observable through multiple sensory modalities. We
also introduce a novel method, called the Amortized Product-of-Experts, to
improve the computational efficiency and the robustness to unseen combinations
of modalities at test time. Experimental results demonstrate that the proposed
model can efficiently infer robust modality-invariant 3D-scene representations
from arbitrary combinations of modalities and perform accurate cross-modal
generation. To perform this exploration, we also develop the Multisensory
Embodied 3D-Scene Environment (MESE). | [
"cs.LG",
"stat.ML"
] |
We present a general framework of semi-supervised dimensionality reduction
for manifold learning which naturally generalizes existing supervised and
unsupervised learning frameworks which apply the spectral decomposition.
Algorithms derived under our framework are able to employ both labeled and
unlabeled examples and are able to handle complex problems where data form
separate clusters of manifolds. Our framework offers simple views, explains
relationships among existing frameworks and provides further extensions which
can improve existing algorithms. Furthermore, a new semi-supervised
kernelization framework called ``KPCA trick'' is proposed to handle non-linear
problems. | [
"cs.LG",
"cs.AI"
] |
The training of autonomous agents often requires expensive and unsafe
trial-and-error interactions with the environment. Nowadays several data sets
containing recorded experiences of intelligent agents performing various tasks,
spanning from the control of unmanned vehicles to human-robot interaction and
medical applications are accessible on the internet. With the intention of
limiting the costs of the learning procedure it is convenient to exploit the
information that is already available rather than collecting new data.
Nevertheless, the incapability to augment the batch can lead the autonomous
agents to develop far from optimal behaviours when the sampled experiences do
not allow for a good estimate of the true distribution of the environment.
Offline learning is the area of machine learning concerned with efficiently
obtaining an optimal policy with a batch of previously collected experiences
without further interaction with the environment. In this paper we adumbrate
the ideas motivating the development of the state-of-the-art offline learning
baselines. The listed methods consist in the introduction of epistemic
uncertainty dependent constraints during the classical resolution of a Markov
Decision Process, with and without function approximators, that aims to
alleviate the bad effects of the distributional mismatch between the available
samples and real world. We provide comments on the practical utility of the
theoretical bounds that justify the application of these algorithms and suggest
the utilization of Generative Adversarial Networks to estimate the
distributional shift that affects all of the proposed model-free and
model-based approaches. | [
"cs.LG",
"cs.AI"
] |
This paper investigates the resilience and robustness of Deep Reinforcement
Learning (DRL) policies to adversarial perturbations in the state space. We
first present an approach for the disentanglement of vulnerabilities caused by
representation learning of DRL agents from those that stem from the sensitivity
of the DRL policies to distributional shifts in state transitions. Building on
this approach, we propose two RL-based techniques for quantitative benchmarking
of adversarial resilience and robustness in DRL policies against perturbations
of state transitions. We demonstrate the feasibility of our proposals through
experimental evaluation of resilience and robustness in DQN, A2C, and PPO2
policies trained in the Cartpole environment. | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.SY",
"stat.ML"
] |
Conventional methods for visual assessment of civil infrastructures have
certain limitations, such as subjectivity of the collected data, long
inspection time, and high cost of labor. Although some new technologies i.e.
robotic techniques that are currently in practice can collect objective,
quantified data, the inspectors own expertise is still critical in many
instances since these technologies are not designed to work interactively with
human inspector. This study aims to create a smart, human centered method that
offers significant contributions to infrastructure inspection, maintenance,
management practice, and safety for the bridge owners. By developing a smart
Mixed Reality framework, which can be integrated into a wearable holographic
headset device, a bridge inspector, for example, can automatically analyze a
certain defect such as a crack that he or she sees on an element, display its
dimension information in real-time along with the condition state. Such systems
can potentially decrease the time and cost of infrastructure inspections by
accelerating essential tasks of the inspector such as defect measurement,
condition assessment and data processing to management systems. The human
centered artificial intelligence will help the inspector collect more
quantified and objective data while incorporating inspectors professional
judgement. This study explains in detail the described system and related
methodologies of implementing attention guided semi supervised deep learning
into mixed reality technology, which interacts with the human inspector during
assessment. Thereby, the inspector and the AI will collaborate or communicate
for improved visual inspection. | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG"
] |
Discriminative dictionary learning (DDL) has recently gained significant
attention due to its impressive performance in various pattern classification
tasks. However, the locality of atoms is not fully explored in conventional DDL
approaches which hampers their classification performance. In this paper, we
propose a locality constraint dictionary learning with support vector
discriminative term (LCDL-SV), in which the locality information is preserved
by employing the graph Laplacian matrix of the learned dictionary. To jointly
learn a classifier during the training phase, a support vector discriminative
term is incorporated into the proposed objective function. Moreover, in the
classification stage, the identity of test data is jointly determined by the
regularized residual and the learned multi-class support vector machine.
Finally, the resulting optimization problem is solved by utilizing the
alternative strategy. Experimental results on benchmark databases demonstrate
the superiority of our proposed method over previous dictionary learning
approaches on both hand-crafted and deep features. The source code of our
proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV | [
"cs.CV"
] |
Change detection is a basic task of remote sensing image processing. The
research objective is to identity the change information of interest and filter
out the irrelevant change information as interference factors. Recently, the
rise of deep learning has provided new tools for change detection, which have
yielded impressive results. However, the available methods focus mainly on the
difference information between multitemporal remote sensing images and lack
robustness to pseudo-change information. To overcome the lack of resistance of
current methods to pseudo-changes, in this paper, we propose a new method,
namely, dual attentive fully convolutional Siamese networks (DASNet) for change
detection in high-resolution images. Through the dual-attention mechanism,
long-range dependencies are captured to obtain more discriminant feature
representations to enhance the recognition performance of the model. Moreover,
the imbalanced sample is a serious problem in change detection, i.e. unchanged
samples are much more than changed samples, which is one of the main reasons
resulting in pseudo-changes. We put forward the weighted double margin
contrastive loss to address this problem by punishing the attention to
unchanged feature pairs and increase attention to changed feature pairs. The
experimental results of our method on the change detection dataset (CDD) and
the building change detection dataset (BCDD) demonstrate that compared with
other baseline methods, the proposed method realizes maximum improvements of
2.1\% and 3.6\%, respectively, in the F1 score. Our Pytorch implementation is
available at https://github.com/lehaifeng/DASNet. | [
"cs.CV"
] |
Spectral clustering is a popular algorithm that clusters points using the
eigenvalues and eigenvectors of Laplacian matrices derived from the data. For
years, spectral clustering has been working mysteriously. This paper explains
spectral clustering by dividing it into two categories based on whether the
graph Laplacian is fully connected or not. For a fully connected graph, this
paper demonstrates the dimension reduction part by offering an objective
function: the covariance between the original data points' similarities and the
mapped data points' similarities. For a multi-connected graph, this paper
proves that with a proper $k$, the first $k$ eigenvectors are the indicators of
the connected components. This paper also proves there is an equivalence
between spectral embedding and PCA. | [
"stat.ML",
"cs.LG"
] |
Multi-focus image fusion (MFF) is a popular technique to generate an
all-in-focus image, where all objects in the scene are sharp. However, existing
methods pay little attention to defocus spread effects of the real-world
multi-focus images. Consequently, most of the methods perform badly in the
areas near focus map boundaries. According to the idea that each local region
in the fused image should be similar to the sharpest one among source images,
this paper presents an optimization-based approach to reduce defocus spread
effects. Firstly, a new MFF assessmentmetric is presented by combining the
principle of structure similarity and detected focus maps. Then, MFF problem is
cast into maximizing this metric. The optimization is solved by gradient
ascent. Experiments conducted on the real-world dataset verify superiority of
the proposed model. The codes are available at
https://github.com/xsxjtu/MFF-SSIM. | [
"cs.CV",
"eess.IV"
] |
Extracting temporal relationships over a range of scales is a hallmark of
human perception and cognition -- and thus it is a critical feature of machine
learning applied to real-world problems. Neural networks are either plagued by
the exploding/vanishing gradient problem in recurrent neural networks (RNNs) or
must adjust their parameters to learn the relevant time scales (e.g., in
LSTMs). This paper introduces DeepSITH, a network comprising
biologically-inspired Scale-Invariant Temporal History (SITH) modules in series
with dense connections between layers. SITH modules respond to their inputs
with a geometrically-spaced set of time constants, enabling the DeepSITH
network to learn problems along a continuum of time-scales. We compare DeepSITH
to LSTMs and other recent RNNs on several time series prediction and decoding
tasks. DeepSITH achieves state-of-the-art performance on these problems. | [
"cs.LG"
] |
The problem of retrosynthetic planning can be framed as one player game, in
which the chemist (or a computer program) works backwards from a molecular
target to simpler starting materials though a series of choices regarding which
reactions to perform. This game is challenging as the combinatorial space of
possible choices is astronomical, and the value of each choice remains
uncertain until the synthesis plan is completed and its cost evaluated. Here,
we address this problem using deep reinforcement learning to identify policies
that make (near) optimal reaction choices during each step of retrosynthetic
planning. Using simulated experience or self-play, we train neural networks to
estimate the expected synthesis cost or value of any given molecule based on a
representation of its molecular structure. We show that learned policies based
on this value network outperform heuristic approaches in synthesizing
unfamiliar molecules from available starting materials using the fewest number
of reactions. We discuss how the learned policies described here can be
incorporated into existing synthesis planning tools and how they can be adapted
to changes in the synthesis cost objective or material availability. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Network representation learning in low dimensional vector space has attracted
considerable attention in both academic and industrial domains. Most real-world
networks are dynamic with addition/deletion of nodes and edges. The existing
graph embedding methods are designed for static networks and they cannot
capture evolving patterns in a large dynamic network. In this paper, we propose
a dynamic embedding method, dynnode2vec, based on the well-known graph
embedding method node2vec. Node2vec is a random walk based embedding method for
static networks. Applying static network embedding in dynamic settings has two
crucial problems: 1) Generating random walks for every time step is time
consuming 2) Embedding vector spaces in each timestamp are different. In order
to tackle these challenges, dynnode2vec uses evolving random walks and
initializes the current graph embedding with previous embedding vectors. We
demonstrate the advantages of the proposed dynamic network embedding by
conducting empirical evaluations on several large dynamic network datasets. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
Special cameras that provide useful features for face anti-spoofing are
desirable, but not always an option. In this work we propose a method to
utilize the difference in dynamic appearance between bona fide and spoof
samples by creating artificial modalities from RGB videos. We introduce two
types of artificial transforms: rank pooling and optical flow, combined in
end-to-end pipeline for spoof detection. We demonstrate that using intermediate
representations that contain less identity and fine-grained features increase
model robustness to unseen attacks as well as to unseen ethnicities. The
proposed method achieves state-of-the-art on the largest cross-ethnicity face
anti-spoofing dataset CASIA-SURF CeFA (RGB). | [
"cs.CV"
] |
We study a general class of contextual bandits, where each context-action
pair is associated with a raw feature vector, but the reward generating
function is unknown. We propose a novel learning algorithm that transforms the
raw feature vector using the last hidden layer of a deep ReLU neural network
(deep representation learning), and uses an upper confidence bound (UCB)
approach to explore in the last linear layer (shallow exploration). We prove
that under standard assumptions, our proposed algorithm achieves
$\tilde{O}(\sqrt{T})$ finite-time regret, where $T$ is the learning time
horizon. Compared with existing neural contextual bandit algorithms, our
approach is computationally much more efficient since it only needs to explore
in the last layer of the deep neural network. | [
"cs.LG",
"stat.ML"
] |
Feature selection aims to select a subset of features to optimize the
performances of downstream predictive tasks. Recently, multi-agent reinforced
feature selection (MARFS) has been introduced to automate feature selection, by
creating agents for each feature to select or deselect corresponding features.
Although MARFS enjoys the automation of the selection process, MARFS suffers
from not just the data complexity in terms of contents and dimensionality, but
also the exponentially-increasing computational costs with regard to the number
of agents. The raised concern leads to a new research question: Can we simplify
the selection process of agents under reinforcement learning context so as to
improve the efficiency and costs of feature selection? To address the question,
we develop a single-agent reinforced feature selection approach integrated with
restructured choice strategy. Specifically, the restructured choice strategy
includes: 1) we exploit only one single agent to handle the selection task of
multiple features, instead of using multiple agents. 2) we develop a scanning
method to empower the single agent to make multiple selection/deselection
decisions in each round of scanning. 3) we exploit the relevance to predictive
labels of features to prioritize the scanning orders of the agent for multiple
features. 4) we propose a convolutional auto-encoder algorithm, integrated with
the encoded index information of features, to improve state representation. 5)
we design a reward scheme that take into account both prediction accuracy and
feature redundancy to facilitate the exploration process. Finally, we present
extensive experimental results to demonstrate the efficiency and effectiveness
of the proposed method. | [
"cs.LG",
"stat.ML"
] |
Early recognition of risky trajectories during an Intensive Care Unit (ICU)
stay is one of the key steps towards improving patient survival. Learning
trajectories from physiological signals continuously measured during an ICU
stay requires learning time-series features that are robust and discriminative
across diverse patient populations. Patients within different ICU populations
(referred here as domains) vary by age, conditions and interventions. Thus,
mortality prediction models using patient data from a particular ICU population
may perform suboptimally in other populations because the features used to
train such models have different distributions across the groups. In this
paper, we explore domain adaptation strategies in order to learn mortality
prediction models that extract and transfer complex temporal features from
multivariate time-series ICU data. Features are extracted in a way that the
state of the patient in a certain time depends on the previous state. This
enables dynamic predictions and creates a mortality risk space that describes
the risk of a patient at a particular time. Experiments based on cross-ICU
populations reveals that our model outperforms all considered baselines. Gains
in terms of AUC range from 4% to 8% for early predictions when compared with a
recent state-of-the-art representative for ICU mortality prediction. In
particular, models for the Cardiac ICU population achieve AUC numbers as high
as 0.88, showing excellent clinical utility for early mortality prediction.
Finally, we present an explanation of factors contributing to the possible ICU
outcomes, so that our models can be used to complement clinical reasoning. | [
"cs.LG",
"stat.ML"
] |
Few-shot learning is often motivated by the ability of humans to learn new
tasks from few examples. However, standard few-shot classification benchmarks
assume that the representation is learned on a limited amount of base class
data, ignoring the amount of prior knowledge that a human may have accumulated
before learning new tasks. At the same time, even if a powerful representation
is available, it may happen in some domain that base class data are limited or
non-existent. This motivates us to study a problem where the representation is
obtained from a classifier pre-trained on a large-scale dataset of a different
domain, assuming no access to its training process, while the base class data
are limited to few examples per class and their role is to adapt the
representation to the domain at hand rather than learn from scratch. We adapt
the representation in two stages, namely on the few base class data if
available and on the even fewer data of new tasks. In doing so, we obtain from
the pre-trained classifier a spatial attention map that allows focusing on
objects and suppressing background clutter. This is important in the new
problem, because when base class data are few, the network cannot learn where
to focus implicitly. We also show that a pre-trained network may be easily
adapted to novel classes, without meta-learning. | [
"cs.CV"
] |
Recognition of document images have important applications in restoring old
and classical texts. The problem involves quality improvement before passing it
to a properly trained OCR to get accurate recognition of the text. The image
enhancement and quality improvement constitute important steps as subsequent
recognition depends upon the quality of the input image. There are scenarios
when high resolution images are not available and our experiments show that the
OCR accuracy reduces significantly with decrease in the spatial resolution of
document images. Thus the only option is to improve the resolution of such
document images. The goal is to construct a high resolution image, given a
single low resolution binary image, which constitutes the problem of single
image super-resolution. Most of the previous work in super-resolution deal with
natural images which have more information-content than the document images.
Here, we use Convolution Neural Network to learn the mapping between low and
the corresponding high resolution images. We experiment with different number
of layers, parameter settings and non-linear functions to build a fast
end-to-end framework for document image super-resolution. Our proposed model
shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images,
resulting in a 3 % improvement of word level accuracy by the OCR. It takes less
time than the recent sparse based natural image super-resolution technique,
making it useful for real-time document recognition applications. | [
"cs.CV"
] |
In this paper, we propose a distributed off-policy actor critic method to
solve multi-agent reinforcement learning problems. Specifically, we assume that
all agents keep local estimates of the global optimal policy parameter and
update their local value function estimates independently. Then, we introduce
an additional consensus step to let all the agents asymptotically achieve
agreement on the global optimal policy function. The convergence analysis of
the proposed algorithm is provided and the effectiveness of the proposed
algorithm is validated using a distributed resource allocation example.
Compared to relevant distributed actor critic methods, here the agents do not
share information about their local tasks, but instead they coordinate to
estimate the global policy function. | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] |
Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount
of interest in the past few decades, while one of the most critical operations
in these systems is the perception of the environment. Deep learning and,
especially, the use of Deep Neural Networks (DNNs) provides impressive results
in analyzing and understanding complex and dynamic scenes from visual data. The
prediction horizons for those perception systems are very short and inference
must often be performed in real time, stressing the need of transforming the
original large pre-trained networks into new smaller models, by utilizing Model
Compression and Acceleration (MCA) techniques. Our goal in this work is to
investigate best practices for appropriately applying novel weight sharing
techniques, optimizing the available variables and the training procedures
towards the significant acceleration of widely adopted DNNs. Extensive
evaluation studies carried out using various state-of-the-art DNN models in
object detection and tracking experiments, provide details about the type of
errors that manifest after the application of weight sharing techniques,
resulting in significant acceleration gains with negligible accuracy losses. | [
"cs.CV",
"cs.LG"
] |
Convolutional layers are an integral part of many deep neural network
solutions in computer vision. Recent work shows that replacing the standard
convolution operation with mechanisms based on self-attention leads to improved
performance on image classification and object detection tasks. In this work,
we show how attention mechanisms can be used to replace another canonical
operation: strided transposed convolution. We term our novel attention-based
operation attention-based upsampling since it increases/upsamples the spatial
dimensions of the feature maps. Through experiments on single image
super-resolution and joint-image upsampling tasks, we show that attention-based
upsampling consistently outperforms traditional upsampling methods based on
strided transposed convolution or based on adaptive filters while using fewer
parameters. We show that the inherent flexibility of the attention mechanism,
which allows it to use separate sources for calculating the attention
coefficients and the attention targets, makes attention-based upsampling a
natural choice when fusing information from multiple image modalities. | [
"cs.CV",
"cs.LG"
] |
Attention mechanisms, especially self-attention, have played an increasingly
important role in deep feature representation for visual tasks. Self-attention
updates the feature at each position by computing a weighted sum of features
using pair-wise affinities across all positions to capture the long-range
dependency within a single sample. However, self-attention has quadratic
complexity and ignores potential correlation between different samples. This
paper proposes a novel attention mechanism which we call external attention,
based on two external, small, learnable, shared memories, which can be
implemented easily by simply using two cascaded linear layers and two
normalization layers; it conveniently replaces self-attention in existing
popular architectures. External attention has linear complexity and implicitly
considers the correlations between all data samples. We further incorporate the
multi-head mechanism into external attention to provide an all-MLP
architecture, external attention MLP (EAMLP), for image classification.
Extensive experiments on image classification, object detection, semantic
segmentation, instance segmentation, image generation, and point cloud analysis
reveal that our method provides results comparable or superior to the
self-attention mechanism and some of its variants, with much lower
computational and memory costs. | [
"cs.CV"
] |
We aim to enable robots to visually localize a target person through the aid
of an additional sensing modality -- the target person's 3D inertial
measurements. The need for such technology may arise when a robot is to meet
person in a crowd for the first time or when an autonomous vehicle must
rendezvous with a rider amongst a crowd without knowing the appearance of the
person in advance. A person's inertial information can be measured with a
wearable device such as a smart-phone and can be shared selectively with an
autonomous system during the rendezvous. We propose a method to learn a
visual-inertial feature space in which the motion of a person in video can be
easily matched to the motion measured by a wearable inertial measurement unit
(IMU). The transformation of the two modalities into the joint feature space is
learned through the use of a contrastive loss which forces inertial motion
features and video motion features generated by the same person to lie close in
the joint feature space. To validate our approach, we compose a dataset of over
60,000 video segments of moving people along with wearable IMU data. Our
experiments show that our proposed method is able to accurately localize a
target person with 80.7% accuracy using only 5 seconds of IMU data and video. | [
"cs.CV",
"cs.RO"
] |
In this paper, we generate and control semantically interpretable filters
that are directly learned from natural images in an unsupervised fashion. Each
semantic filter learns a visually interpretable local structure in conjunction
with other filters. The significance of learning these interpretable filter
sets is demonstrated on two contrasting applications. The first application is
image recognition under progressive decolorization, in which recognition
algorithms should be color-insensitive to achieve a robust performance. The
second application is image quality assessment where objective methods should
be sensitive to color degradations. In the proposed work, the sensitivity and
lack thereof are controlled by weighing the semantic filters based on the local
structures they represent. To validate the proposed approach, we utilize the
CURE-TSR dataset for image recognition and the TID 2013 dataset for image
quality assessment. We show that the proposed semantic filter set achieves
state-of-the-art performances in both datasets while maintaining its robustness
across progressive distortions. | [
"cs.CV",
"eess.IV",
"I.2; I.4; I.5"
] |
Proximal Policy Optimization (PPO) is among the most widely used algorithms
in reinforcement learning, which achieves state-of-the-art performance in many
challenging problems. The keys to its success are the reliable policy updates
through the clipping mechanism and the multiple epochs of minibatch updates.
The aim of this research is to give new simple but effective alternatives to
the former. For this, we propose linearly and exponentially decaying clipping
range approaches throughout the training. With these, we would like to provide
higher exploration at the beginning and stronger restrictions at the end of the
learning phase. We investigate their performance in several classical control
and locomotive robotic environments. During the analysis, we found that they
influence the achieved rewards and are effective alternatives to the constant
clipping method in many reinforcement learning tasks. | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
Clustering is a fundamental task in unsupervised learning, one that targets
to group a dataset into clusters of similar objects. There has been recent
interest in embedding normative considerations around fairness within
clustering formulations. In this paper, we propose 'local connectivity' as a
crucial factor in assessing membership desert in centroid clustering. We use
local connectivity to refer to the support offered by the local neighborhood of
an object towards supporting its membership to the cluster in question. We
motivate the need to consider local connectivity of objects in cluster
assignment, and provide ways to quantify local connectivity in a given
clustering. We then exploit concepts from density-based clustering and devise
LOFKM, a clustering method that seeks to deepen local connectivity in
clustering outputs, while staying within the framework of centroid clustering.
Through an empirical evaluation over real-world datasets, we illustrate that
LOFKM achieves notable improvements in local connectivity at reasonable costs
to clustering quality, illustrating the effectiveness of the method. | [
"cs.LG",
"cs.AI"
] |
A key problem in location-based modeling and forecasting lies in identifying
suitable spatial and temporal resolutions. In particular, judicious spatial
partitioning can play a significant role in enhancing the performance of
location-based forecasting models. In this work, we investigate two widely used
tessellation strategies for partitioning city space, in the context of
real-time taxi demand forecasting. Our study compares (i) Geohash tessellation,
and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over
multiple time scales. For the purpose of comparison, we employ classical
time-series tools to model the spatio-temporal demand. Our study finds that the
performance of each tessellation strategy is highly dependent on the city
geography, spatial distribution of the data, and the time of the day, and that
neither strategy is found to perform optimally across the forecast horizon. We
propose a hybrid tessellation algorithm that picks the best tessellation
strategy at each instant, based on their performance in the recent past. Our
hybrid algorithm is a non-stationary variant of the well-known HEDGE algorithm
for choosing the best advice from multiple experts. We show that the hybrid
tessellation strategy performs consistently better than either of the two
strategies across the data sets considered, at multiple time scales, and with
different performance metrics. We achieve an average accuracy of above 80% per
km^2 for both data sets considered at 60 minute aggregation levels. | [
"cs.LG",
"stat.AP",
"stat.ML"
] |
By means of a local surrogate approach, an analytical method to yield
explanations of AI-predictions in the framework of regression models is
defined. In the case of the AI-model producing additive corrections to the
predictions of a base model, the explanations are delivered in the form of a
shift of its interpretable parameters as long as the AI- predictions are small
in a rigorously defined sense. Criteria are formulated giving a precise
relation between lost accuracy and lacking model fidelity. Two applications
show how physical or econometric parameters may be used to interpret the action
of neural network and random forest models in the sense of the underlying base
model. This is an extended version of our paper presented at the ISM 2020
conference, where we first introduced our new approach BAPC. | [
"stat.ML",
"cs.LG"
] |
Autonomous detection of desired events from large databases using time series
classification is becoming increasingly important in civil engineering as a
result of continued long-term health monitoring of a large number of
engineering structures encompassing buildings, bridges, towers, and offshore
platforms. In this context, this paper proposes the application of a relatively
new time series representation named "Shapelet transform", which is based on
local similarity in the shape of the time series subsequences. In consideration
of the individual attributes distinctive to time series signals in earthquake,
wind and ocean engineering, the application of this transform yields a new
shape-based feature representation. Combining this shape-based representation
with a standard machine learning algorithm, a truly "white-box" machine
learning model is proposed with understandable features and a transparent
algorithm. This model automates event detection without the intervention of
domain practitioners, yielding a practical event detection procedure. The
efficacy of this proposed shapelet transform-based autonomous detection
procedure is demonstrated by examples, to identify known and unknown earthquake
events from continuously recorded ground-motion measurements, to detect pulses
in the velocity time history of ground motions to distinguish between
near-field and far-field ground motions, to identify thunderstorms from
continuous wind speed measurements, to detect large-amplitude wind-induced
vibrations from the bridge monitoring data, and to identify plunging breaking
waves that have a significant impact on offshore structures. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
A video-grounded dialogue system referred to as the Structured Co-reference
Graph Attention (SCGA) is presented for decoding the answer sequence to a
question regarding a given video while keeping track of the dialogue context.
Although recent efforts have made great strides in improving the quality of the
response, performance is still far from satisfactory. The two main challenging
issues are as follows: (1) how to deduce co-reference among multiple modalities
and (2) how to reason on the rich underlying semantic structure of video with
complex spatial and temporal dynamics. To this end, SCGA is based on (1)
Structured Co-reference Resolver that performs dereferencing via building a
structured graph over multiple modalities, (2) Spatio-temporal Video Reasoner
that captures local-to-global dynamics of video via gradually neighboring graph
attention. SCGA makes use of pointer network to dynamically replicate parts of
the question for decoding the answer sequence. The validity of the proposed
SCGA is demonstrated on AVSD@DSTC7 and AVSD@DSTC8 datasets, a challenging
video-grounded dialogue benchmarks, and TVQA dataset, a large-scale videoQA
benchmark. Our empirical results show that SCGA outperforms other
state-of-the-art dialogue systems on both benchmarks, while extensive ablation
study and qualitative analysis reveal performance gain and improved
interpretability. | [
"cs.CV"
] |
We aim to create a framework for transfer learning using latent factor models
to learn the dependence structure between a larger source dataset and a target
dataset. The methodology is motivated by our goal of building a risk-assessment
model for surgery patients, using both institutional and national surgical
outcomes data. The national surgical outcomes data is collected through NSQIP
(National Surgery Quality Improvement Program), a database housing almost 4
million patients from over 700 different hospitals. We build a latent factor
model with a hierarchical prior on the loadings matrix to appropriately account
for the different covariance structure in our data. We extend this model to
handle more complex relationships between the populations by deriving a scale
mixture formulation using stick-breaking properties. Our model provides a
transfer learning framework that utilizes all information from both the source
and target data, while modeling the underlying inherent differences between
them. | [
"stat.ML"
] |
We focus our attention on the problem of generating adversarial perturbations
based on the gradient in image classification domain | [
"cs.CV",
"cs.CR",
"cs.LG"
] |
The automatic semantic segmentation of the huge amount of acquired remote
sensing data has become an important task in the last decade. Images and Point
Clouds (PCs) are fundamental data representations, particularly in urban
mapping applications. Textured 3D meshes integrate both data representations
geometrically by wiring the PC and texturing the surface elements with
available imagery. We present a mesh-centered holistic geometry-driven
methodology that explicitly integrates entities of imagery, PC and mesh. Due to
its integrative character, we choose the mesh as the core representation that
also helps to solve the visibility problem for points in imagery. Utilizing the
proposed multi-modal fusion as the backbone and considering the established
entity relationships, we enable the sharing of information across the
modalities imagery, PC and mesh in a two-fold manner: (i) feature transfer and
(ii) label transfer. By these means, we achieve to enrich feature vectors to
multi-modal feature vectors for each representation. Concurrently, we achieve
to label all representations consistently while reducing the manual label
effort to a single representation. Consequently, we facilitate to train machine
learning algorithms and to semantically segment any of these data
representations - both in a multi-modal and single-modal sense. The paper
presents the association mechanism and the subsequent information transfer,
which we believe are cornerstones for multi-modal scene analysis. Furthermore,
we discuss the preconditions and limitations of the presented approach in
detail. We demonstrate the effectiveness of our methodology on the ISPRS 3D
semantic labeling contest (Vaihingen 3D) and a proprietary data set (Hessigheim
3D). | [
"cs.CV"
] |
Existing Neural Architecture Search (NAS) methods either encode neural
architectures using discrete encodings that do not scale well, or adopt
supervised learning-based methods to jointly learn architecture representations
and optimize architecture search on such representations which incurs search
bias. Despite the widespread use, architecture representations learned in NAS
are still poorly understood. We observe that the structural properties of
neural architectures are hard to preserve in the latent space if architecture
representation learning and search are coupled, resulting in less effective
search performance. In this work, we find empirically that pre-training
architecture representations using only neural architectures without their
accuracies as labels considerably improve the downstream architecture search
efficiency. To explain these observations, we visualize how unsupervised
architecture representation learning better encourages neural architectures
with similar connections and operators to cluster together. This helps to map
neural architectures with similar performance to the same regions in the latent
space and makes the transition of architectures in the latent space relatively
smooth, which considerably benefits diverse downstream search strategies. | [
"cs.CV",
"cs.LG"
] |
Neuro-symbolic representations have proved effective in learning structure
information in vision and language. In this paper, we propose a new model
architecture for learning multi-modal neuro-symbolic representations for video
captioning. Our approach uses a dictionary learning-based method of learning
relations between videos and their paired text descriptions. We refer to these
relations as relative roles and leverage them to make each token role-aware
using attention. This results in a more structured and interpretable
architecture that incorporates modality-specific inductive biases for the
captioning task. Intuitively, the model is able to learn spatial, temporal, and
cross-modal relations in a given pair of video and text. The disentanglement
achieved by our proposal gives the model more capacity to capture multi-modal
structures which result in captions with higher quality for videos. Our
experiments on two established video captioning datasets verifies the
effectiveness of the proposed approach based on automatic metrics. We further
conduct a human evaluation to measure the grounding and relevance of the
generated captions and observe consistent improvement for the proposed model.
The codes and trained models can be found at
https://github.com/hassanhub/R3Transformer | [
"cs.CV",
"cs.AI",
"eess.IV"
] |
We present the first differentiable Network Architecture Search (NAS) for
Graph Neural Networks (GNNs). GNNs show promising performance on a wide range
of tasks, but require a large amount of architecture engineering. First, graphs
are inherently a non-Euclidean and sophisticated data structure, leading to
poor adaptivity of GNN architectures across different datasets. Second, a
typical graph block contains numerous different components, such as aggregation
and attention, generating a large combinatorial search space. To counter these
problems, we propose a Probabilistic Dual Network Architecture Search (PDNAS)
framework for GNNs. PDNAS not only optimises the operations within a single
graph block (micro-architecture), but also considers how these blocks should be
connected to each other (macro-architecture). The dual architecture (micro- and
marco-architectures) optimisation allows PDNAS to find deeper GNNs on diverse
datasets with better performance compared to other graph NAS methods. Moreover,
we use a fully gradient-based search approach to update architectural
parameters, making it the first differentiable graph NAS method. PDNAS
outperforms existing hand-designed GNNs and NAS results, for example, on the
PPI dataset, PDNAS beats its best competitors by 1.67 and 0.17 in F1 scores. | [
"cs.LG",
"stat.ML"
] |
We present a novel approach to automatic image colorization by imitating the
imagination process of human experts. Our imagination module is designed to
generate color images that are context-correlated with black-and-white photos.
Given a black-and-white image, our imagination module firstly extracts the
context information, which is then used to synthesize colorful and diverse
images using a conditional image synthesis network (e.g., semantic image
synthesis model). We then design a colorization module to colorize the
black-and-white images with the guidance of imagination for photorealistic
colorization. Experimental results show that our work produces more colorful
and diverse results than state-of-the-art image colorization methods. Our
source codes will be publicly available. | [
"cs.CV"
] |
$ $Let $F$ be a multivariate function from a product set $\Sigma^n$ to an
Abelian group $G$. A $k$-partition of $F$ with cost $\delta$ is a partition of
the set of variables $\mathbf{V}$ into $k$ non-empty subsets $(\mathbf{X}_1,
\dots, \mathbf{X}_k)$ such that $F(\mathbf{V})$ is $\delta$-close to
$F_1(\mathbf{X}_1)+\dots+F_k(\mathbf{X}_k)$ for some $F_1, \dots, F_k$ with
respect to a given error metric. We study algorithms for agnostically learning
$k$ partitions and testing $k$-partitionability over various groups and error
metrics given query access to $F$. In particular we show that
$1.$ Given a function that has a $k$-partition of cost $\delta$, a partition
of cost $\mathcal{O}(k n^2)(\delta + \epsilon)$ can be learned in time
$\tilde{\mathcal{O}}(n^2 \mathrm{poly} (1/\epsilon))$ for any $\epsilon > 0$.
In contrast, for $k = 2$ and $n = 3$ learning a partition of cost $\delta +
\epsilon$ is NP-hard.
$2.$ When $F$ is real-valued and the error metric is the 2-norm, a
2-partition of cost $\sqrt{\delta^2 + \epsilon}$ can be learned in time
$\tilde{\mathcal{O}}(n^5/\epsilon^2)$.
$3.$ When $F$ is $\mathbb{Z}_q$-valued and the error metric is Hamming
weight, $k$-partitionability is testable with one-sided error and
$\mathcal{O}(kn^3/\epsilon)$ non-adaptive queries. We also show that even
two-sided testers require $\Omega(n)$ queries when $k = 2$.
This work was motivated by reinforcement learning control tasks in which the
set of control variables can be partitioned. The partitioning reduces the task
into multiple lower-dimensional ones that are relatively easier to learn. Our
second algorithm empirically increases the scores attained over previous
heuristic partitioning methods applied in this context. | [
"cs.LG",
"cs.DS",
"stat.ML"
] |
Molecular optimization, which transforms a given input molecule X into
another Y with desirable properties, is essential in molecular drug discovery.
The traditional translating approaches, generating the molecular graphs from
scratch by adding some substructures piece by piece, prone to error because of
the large set of candidate substructures in a large number of steps to the
final target. In this study, we present a novel molecular optimization
paradigm, Graph Polish, which changes molecular optimization from the
traditional "two-language translating" task into a "single-language polishing"
task. The key to this optimization paradigm is to find an optimization center
subject to the conditions that the preserved areas around it ought to be
maximized and thereafter the removed and added regions should be minimized. We
then propose an effective and efficient learning framework T&S polish to
capture the long-term dependencies in the optimization steps. The T component
automatically identifies and annotates the optimization centers and the
preservation, removal and addition of some parts of the molecule, and the S
component learns these behaviors and applies these actions to a new molecule.
Furthermore, the proposed paradigm can offer an intuitive interpretation for
each molecular optimization result. Experiments with multiple optimization
tasks are conducted on four benchmark datasets. The proposed T&S polish
approach achieves significant advantage over the five state-of-the-art baseline
methods on all the tasks. In addition, extensive studies are conducted to
validate the effectiveness, explainability and time saving of the novel
optimization paradigm. | [
"cs.LG",
"stat.ML"
] |
Currently, instance segmentation is attracting more and more attention in
machine learning region. However, there exists some defects on the information
propagation in previous Mask R-CNN and other network models. In this paper, we
propose supervised adaptive threshold network for instance segmentation.
Specifically, we adopt the Mask R-CNN method based on adaptive threshold, and
by establishing a layered adaptive network structure, it performs adaptive
binarization on the probability graph generated by Mask RCNN to obtain better
segmentation effect and reduce the error rate. At the same time, an adaptive
feature pool is designed to make the transmission between different layers of
the network more accurate and effective, reduce the loss in the process of
feature transmission, and further improve the mask method. Experiments on
benchmark data sets indicate that the effectiveness of the proposed model | [
"cs.CV"
] |
The segmentation of medical images is a fundamental step in automated
clinical decision support systems. Existing medical image segmentation methods
based on supervised deep learning, however, remain problematic because of their
reliance on large amounts of labelled training data. Although medical imaging
data repositories continue to expand, there has not been a commensurate
increase in the amount of annotated data. Hence, we propose a new spatial
guided self-supervised clustering network (SGSCN) for medical image
segmentation, where we introduce multiple loss functions designed to aid in
grouping image pixels that are spatially connected and have similar feature
representations. It iteratively learns feature representations and clustering
assignment of each pixel in an end-to-end fashion from a single image. We also
propose a context-based consistency loss that better delineates the shape and
boundaries of image regions. It enforces all the pixels belonging to a cluster
to be spatially close to the cluster centre. We evaluated our method on 2
public medical image datasets and compared it to existing conventional and
self-supervised clustering methods. Experimental results show that our method
was most accurate for medical image segmentation. | [
"cs.CV"
] |
In this paper, we revisit implicit regularization from the ground up using
notions from dynamical systems and invariant subspaces of Morse functions. The
key contributions are a new criterion for implicit regularization---a leading
contender to explain the generalization power of deep models such as neural
networks---and a general blueprint to study it. We apply these techniques to
settle a conjecture on implicit regularization in matrix factorization. | [
"cs.LG",
"stat.ML"
] |
Fooling people with highly realistic fake images generated with Deepfake or
GANs brings a great social disturbance to our society. Many methods have been
proposed to detect fake images, but they are vulnerable to adversarial
perturbations -- intentionally designed noises that can lead to the wrong
prediction. Existing methods of attacking fake image detectors usually generate
adversarial perturbations to perturb almost the entire image. This is redundant
and increases the perceptibility of perturbations. In this paper, we propose a
novel method to disrupt the fake image detection by determining key pixels to a
fake image detector and attacking only the key pixels, which results in the
$L_0$ and the $L_2$ norms of adversarial perturbations much less than those of
existing works. Experiments on two public datasets with three fake image
detectors indicate that our proposed method achieves state-of-the-art
performance in both white-box and black-box attacks. | [
"cs.CV",
"cs.AI"
] |
Crowd monitoring and analysis in mass events are highly important
technologies to support the security of attending persons. Proposed methods
based on terrestrial or airborne image/video data often fail in achieving
sufficiently accurate results to guarantee a robust service. We present a novel
framework for estimating human count, density and motion from video data based
on custom tailored object detection techniques, a regression based density
estimate and a total variation based optical flow extraction. From the gathered
features we present a detailed accuracy analysis versus ground truth
measurements. In addition, all information is projected into world coordinates
to enable a direct integration with existing geo-information systems. The
resulting human counts demonstrate a mean error of 4% to 9% and thus represent
a most efficient measure that can be robustly applied in security critical
services. | [
"cs.CV"
] |
Localization of chest pathologies in chest X-ray images is a challenging task
because of their varying sizes and appearances. We propose a novel weakly
supervised method to localize chest pathologies using class aware deep
multiscale feature learning. Our method leverages intermediate feature maps
from CNN layers at different stages of a deep network during the training of a
classification model using image level annotations of pathologies. During the
training phase, a set of \emph{layer relevance weights} are learned for each
pathology class and the CNN is optimized to perform pathology classification by
convex combination of feature maps from both shallow and deep layers using the
learned weights. During the test phase, to localize the predicted pathology,
the multiscale attention map is obtained by convex combination of class
activation maps from each stage using the \emph{layer relevance weights}
learned during the training phase. We have validated our method using 112000
X-ray images and compared with the state-of-the-art localization methods. We
experimentally demonstrate that the proposed weakly supervised method can
improve the localization performance of small pathologies such as nodule and
mass while giving comparable performance for bigger pathologies e.g.,
Cardiomegaly | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
A longstanding problem in machine learning is to find unsupervised methods
that can learn the statistical structure of high dimensional signals. In recent
years, GANs have gained much attention as a possible solution to the problem,
and in particular have shown the ability to generate remarkably realistic high
resolution sampled images. At the same time, many authors have pointed out that
GANs may fail to model the full distribution ("mode collapse") and that using
the learned models for anything other than generating samples may be very
difficult. In this paper, we examine the utility of GANs in learning
statistical models of images by comparing them to perhaps the simplest
statistical model, the Gaussian Mixture Model. First, we present a simple
method to evaluate generative models based on relative proportions of samples
that fall into predetermined bins. Unlike previous automatic methods for
evaluating models, our method does not rely on an additional neural network nor
does it require approximating intractable computations. Second, we compare the
performance of GANs to GMMs trained on the same datasets. While GMMs have
previously been shown to be successful in modeling small patches of images, we
show how to train them on full sized images despite the high dimensionality.
Our results show that GMMs can generate realistic samples (although less sharp
than those of GANs) but also capture the full distribution, which GANs fail to
do. Furthermore, GMMs allow efficient inference and explicit representation of
the underlying statistical structure. Finally, we discuss how GMMs can be used
to generate sharp images. | [
"cs.CV",
"cs.LG"
] |
Applications of perceptual image quality assessment (IQA) in image and video
processing, such as image acquisition, image compression, image restoration and
multimedia communication, have led to the development of many IQA metrics. In
this paper, a reliable full reference IQA model is proposed that utilize
gradient similarity (GS), chromaticity similarity (CS), and deviation pooling
(DP). By considering the shortcomings of the commonly used GS to model human
visual system (HVS), a new GS is proposed through a fusion technique that is
more likely to follow HVS. We propose an efficient and effective formulation to
calculate the joint similarity map of two chromatic channels for the purpose of
measuring color changes. In comparison with a commonly used formulation in the
literature, the proposed CS map is shown to be more efficient and provide
comparable or better quality predictions. Motivated by a recent work that
utilizes the standard deviation pooling, a general formulation of the DP is
presented in this paper and used to compute a final score from the proposed GS
and CS maps. This proposed formulation of DP benefits from the Minkowski
pooling and a proposed power pooling as well. The experimental results on six
datasets of natural images, a synthetic dataset, and a digitally retouched
dataset show that the proposed index provides comparable or better quality
predictions than the most recent and competing state-of-the-art IQA metrics in
the literature, it is reliable and has low complexity. The MATLAB source code
of the proposed metric is available at
https://www.mathworks.com/matlabcentral/fileexchange/59809. | [
"cs.CV"
] |
There are great interests as well as many challenges in applying
reinforcement learning (RL) to recommendation systems. In this setting, an
online user is the environment; neither the reward function nor the environment
dynamics are clearly defined, making the application of RL challenging. In this
paper, we propose a novel model-based reinforcement learning framework for
recommendation systems, where we develop a generative adversarial network to
imitate user behavior dynamics and learn her reward function. Using this user
model as the simulation environment, we develop a novel Cascading DQN algorithm
to obtain a combinatorial recommendation policy which can handle a large number
of candidate items efficiently. In our experiments with real data, we show this
generative adversarial user model can better explain user behavior than
alternatives, and the RL policy based on this model can lead to a better
long-term reward for the user and higher click rate for the system. | [
"cs.LG",
"cs.IR",
"stat.ML"
] |
Feed-forward neural networks consist of a sequence of layers, in which each
layer performs some processing on the information from the previous layer. A
downside to this approach is that each layer (or module, as multiple modules
can operate in parallel) is tasked with processing the entire hidden state,
rather than a particular part of the state which is most relevant for that
module. Methods which only operate on a small number of input variables are an
essential part of most programming languages, and they allow for improved
modularity and code re-usability. Our proposed method, Neural Function Modules
(NFM), aims to introduce the same structural capability into deep learning.
Most of the work in the context of feed-forward networks combining top-down and
bottom-up feedback is limited to classification problems. The key contribution
of our work is to combine attention, sparsity, top-down and bottom-up feedback,
in a flexible algorithm which, as we show, improves the results in standard
classification, out-of-domain generalization, generative modeling, and learning
representations in the context of reinforcement learning. | [
"cs.LG",
"stat.ML"
] |
Unsupervised learning of optical flow, which leverages the supervision from
view synthesis, has emerged as a promising alternative to supervised methods.
However, the objective of unsupervised learning is likely to be unreliable in
challenging scenes. In this work, we present a framework to use more reliable
supervision from transformations. It simply twists the general unsupervised
learning pipeline by running another forward pass with transformed data from
augmentation, along with using transformed predictions of original data as the
self-supervision signal. Besides, we further introduce a lightweight network
with multiple frames by a highly-shared flow decoder. Our method consistently
gets a leap of performance on several benchmarks with the best accuracy among
deep unsupervised methods. Also, our method achieves competitive results to
recent fully supervised methods while with much fewer parameters. | [
"cs.CV"
] |
Deep learning system have drawback that their output is not accompanied with
ex-planation. In a domain such as forensic handwriting verification it is
essential to provideexplanation to jurors. The goal of handwriting verification
is to find a measure of confi-dence whether the given handwritten samples are
written by the same or different writer.We propose a method to generate
explanations for the confidence provided by convolu-tional neural network (CNN)
which maps the input image to 15 annotations (features)provided by experts. Our
system comprises of: (1) Feature learning network (FLN),a differentiable
system, (2) Inference module for providing explanations. Furthermore,inference
module provides two types of explanations: (a) Based on cosine
similaritybetween categorical probabilities of each feature, (b) Based on
Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We
perform experiments using acombination of feature learning network (FLN) and
each inference module. We evaluateour system using XAI-AND dataset, containing
13700 handwritten samples and 15 cor-responding expert examined features for
each sample. The dataset is released for publicuse and the methods can be
extended to provide explanations on other verification taskslike face
verification and bio-medical comparison. This dataset can serve as the basis
and benchmark for future research in explanation based handwriting
verification. The code is available on github. | [
"cs.CV",
"cs.LG"
] |
A key aspect of VQA models that are interpretable is their ability to ground
their answers to relevant regions in the image. Current approaches with this
capability rely on supervised learning and human annotated groundings to train
attention mechanisms inside the VQA architecture. Unfortunately, obtaining
human annotations specific for visual grounding is difficult and expensive. In
this work, we demonstrate that we can effectively train a VQA architecture with
grounding supervision that can be automatically obtained from available region
descriptions and object annotations. We also show that our model trained with
this mined supervision generates visual groundings that achieve a higher
correlation with respect to manually-annotated groundings, meanwhile achieving
state-of-the-art VQA accuracy. | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] |
Modern top-performing object detectors depend heavily on backbone networks,
whose advances bring consistent performance gains through exploring more
effective network structures. In this paper, we propose a novel and flexible
backbone framework, namely CBNetV2, to construct high-performance detectors
using existing open-sourced pre-trained backbones under the pre-training
fine-tuning paradigm. In particular, CBNetV2 architecture groups multiple
identical backbones, which are connected through composite connections.
Specifically, it integrates the high- and low-level features of multiple
backbone networks and gradually expands the receptive field to more efficiently
perform object detection. We also propose a better training strategy with
assistant supervision for CBNet-based detectors. Without additional
pre-training of the composite backbone, CBNetV2 can be adapted to various
backbones (CNN-based vs. Transformer-based) and head designs of most mainstream
detectors (one-stage vs. two-stage, anchor-based vs. anchor-free-based).
Experiments provide strong evidence that, compared with simply increasing the
depth and width of the network, CBNetV2 introduces a more efficient, effective,
and resource-friendly way to build high-performance backbone networks.
Particularly, our Dual-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO
test-dev under the single-model and single-scale testing protocol, which is
significantly better than the state-of-the-art result (57.7% box AP and 50.2%
mask AP) achieved by Swin-L, while the training schedule is reduced by
6$\times$. With multi-scale testing, we push the current best single model
result to a new record of 60.1% box AP and 52.3% mask AP without using extra
training data. Code is available at https://github.com/VDIGPKU/CBNetV2. | [
"cs.CV"
] |
Cross-domain object detection and semantic segmentation have witnessed
impressive progress recently. Existing approaches mainly consider the domain
shift resulting from external environments including the changes of background,
illumination or weather, while distinct camera intrinsic parameters appear
commonly in different domains, and their influence for domain adaptation has
been very rarely explored. In this paper, we observe that the Field of View
(FoV) gap induces noticeable instance appearance differences between the source
and target domains. We further discover that the FoV gap between two domains
impairs domain adaptation performance under both the FoV-increasing (source FoV
< target FoV) and FoV-decreasing cases. Motivated by the observations, we
propose the \textbf{Position-Invariant Transform} (PIT) to better align images
in different domains. We also introduce a reverse PIT for mapping the
transformed/aligned images back to the original image space and design a loss
re-weighting strategy to accelerate the training process. Our method can be
easily plugged into existing cross-domain detection/segmentation frameworks
while bringing about negligible computational overhead. Extensive experiments
demonstrate that our method can soundly boost the performance on both
cross-domain object detection and segmentation for state-of-the-art techniques.
Our code is available at
https://github.com/sheepooo/PIT-Position-Invariant-Transform. | [
"cs.CV"
] |
Order dispatch is one of the central problems to ride-sharing platforms.
Recently, value-based reinforcement learning algorithms have shown promising
performance on this problem. However, in real-world applications, the
non-stationarity of the demand-supply system poses challenges to re-utilizing
data generated in different time periods to learn the value function. In this
work, motivated by the fact that the relative relationship between the values
of some states is largely stable across various environments, we propose a
pattern transfer learning framework for value-based reinforcement learning in
the order dispatch problem. Our method efficiently captures the value patterns
by incorporating a concordance penalty. The superior performance of the
proposed method is supported by experiments. | [
"cs.LG"
] |
Talking face generation aims to synthesize a face video with precise lip
synchronization as well as a smooth transition of facial motion over the entire
video via the given speech clip and facial image. Most existing methods mainly
focus on either disentangling the information in a single image or learning
temporal information between frames. However, cross-modality coherence between
audio and video information has not been well addressed during synthesis. In
this paper, we propose a novel arbitrary talking face generation framework by
discovering the audio-visual coherence via the proposed Asymmetric Mutual
Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA)
block by selectively focusing the lip area of the input image during the
training stage, to further enhance lip synchronization. Experimental results on
benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods
on prevalent metrics with robust high-resolution synthesizing on gender and
pose variations. | [
"cs.CV"
] |
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their
potential in modeling high-order relations preserved in graph structured data.
However, most existing convolution filters are localized and determined by the
pre-defined initial hypergraph topology, neglecting to explore implicit and
long-ange relations in real-world data. In this paper, we propose the first
learning-based method tailored for constructing adaptive hypergraph structure,
termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a generic
plug-in-play module for improving the representational power of HGCNNs.
Specifically, HERALD adaptively optimizes the adjacency relationship between
hypernodes and hyperedges in an end-to-end manner and thus the task-aware
hypergraph is learned. Furthermore, HERALD employs the self-attention mechanism
to capture the non-local paired-nodes relation. Extensive experiments on
various popular hypergraph datasets for node classification and graph
classification tasks demonstrate that our approach obtains consistent and
considerable performance enhancement, proving its effectiveness and
generalization ability. | [
"cs.LG"
] |
Turbulence is still one of the main challenges for accurately predicting
reactive flows. Therefore, the development of new turbulence closures which can
be applied to combustion problems is essential. Data-driven modeling has become
very popular in many fields over the last years as large, often extensively
labeled, datasets became available and training of large neural networks became
possible on GPUs speeding up the learning process tremendously. However, the
successful application of deep neural networks in fluid dynamics, for example
for subgrid modeling in the context of large-eddy simulations (LESs), is still
challenging. Reasons for this are the large amount of degrees of freedom in
realistic flows, the high requirements with respect to accuracy and error
robustness, as well as open questions, such as the generalization capability of
trained neural networks in such high-dimensional, physics-constrained
scenarios. This work presents a novel subgrid modeling approach based on a
generative adversarial network (GAN), which is trained with unsupervised deep
learning (DL) using adversarial and physics-informed losses. A two-step
training method is used to improve the generalization capability, especially
extrapolation, of the network. The novel approach gives good results in a
priori as well as a posteriori tests with decaying turbulence including
turbulent mixing. The applicability of the network in complex combustion
scenarios is furthermore discussed by employing it to a reactive LES of the
Spray A case defined by the Engine Combustion Network (ECN). | [
"cs.LG",
"cs.GR",
"physics.comp-ph",
"physics.flu-dyn",
"stat.ML"
] |
Self-play, where the algorithm learns by playing against itself without
requiring any direct supervision, has become the new weapon in modern
Reinforcement Learning (RL) for achieving superhuman performance in practice.
However, the majority of exisiting theory in reinforcement learning only
applies to the setting where the agent plays against a fixed environment; it
remains largely open whether self-play algorithms can be provably effective,
especially when it is necessary to manage the exploration/exploitation
tradeoff. We study self-play in competitive reinforcement learning under the
setting of Markov games, a generalization of Markov decision processes to the
two-player case. We introduce a self-play algorithm---Value Iteration with
Upper/Lower Confidence Bound (VI-ULCB)---and show that it achieves regret
$\tilde{\mathcal{O}}(\sqrt{T})$ after playing $T$ steps of the game, where the
regret is measured by the agent's performance against a \emph{fully
adversarial} opponent who can exploit the agent's strategy at \emph{any} step.
We also introduce an explore-then-exploit style algorithm, which achieves a
slightly worse regret of $\tilde{\mathcal{O}}(T^{2/3})$, but is guaranteed to
run in polynomial time even in the worst case. To the best of our knowledge,
our work presents the first line of provably sample-efficient self-play
algorithms for competitive reinforcement learning. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Generative Adversarial Networks (GAN) have demonstrated the potential to
recover realistic details for single image super-resolution (SISR). To further
improve the visual quality of super-resolved results, PIRM2018-SR Challenge
employed perceptual metrics to assess the perceptual quality, such as PI, NIQE,
and Ma. However, existing methods cannot directly optimize these
indifferentiable perceptual metrics, which are shown to be highly correlated
with human ratings. To address the problem, we propose Super-Resolution
Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator
in the direction of perceptual metrics. Specifically, we first train a Ranker
which can learn the behavior of perceptual metrics and then introduce a novel
rank-content loss to optimize the perceptual quality. The most appealing part
is that the proposed method can combine the strengths of different SR methods
to generate better results. Extensive experiments show that RankSRGAN achieves
visually pleasing results and reaches state-of-the-art performance in
perceptual metrics. Project page:
https://wenlongzhang0724.github.io/Projects/RankSRGAN | [
"cs.CV"
] |
Practical reinforcement learning problems are often formulated as constrained
Markov decision process (CMDP) problems, in which the agent has to maximize the
expected return while satisfying a set of prescribed safety constraints. In
this study, we propose a novel simulator-based method to approximately solve a
CMDP problem without making any compromise on the safety constraints. We
achieve this by decomposing the CMDP into a pair of MDPs; reconnaissance MDP
and planning MDP. The purpose of reconnaissance MDP is to evaluate the set of
actions that are safe, and the purpose of planning MDP is to maximize the
return while using the actions authorized by reconnaissance MDP. RMDP can
define a set of safe policies for any given set of safety constraint, and this
set of safe policies can be used to solve another CMDP problem with different
reward. Our method is not only computationally less demanding than the previous
simulator-based approaches to CMDP, but also capable of finding a competitive
reward-seeking policy in a high dimensional environment, including those
involving multiple moving obstacles. | [
"cs.LG",
"stat.ML"
] |
The paper posits a computationally-efficient algorithm for multi-class facial
image classification in which images are constrained with translation,
rotation, scale, color, illumination and affine distortion. The proposed method
is divided into five main building blocks including Haar-Cascade for face
detection, Bilateral Filter for image preprocessing to remove unwanted noise,
Affine Speeded-Up Robust Features (ASURF) for keypoint detection and
description, Vector of Locally Aggregated Descriptors (VLAD) for feature
quantization and Cloud Forest for image classification. The proposed method
aims at improving the accuracy and the time taken for face recognition systems.
The usage of the Cloud Forest algorithm as a classifier on three benchmark
datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising
results. The proposed methodology using Cloud Forest algorithm successfully
improves the recognition model by 2-12\% when differentiated against other
ensemble techniques like the Random Forest classifier depending upon the
dataset used. | [
"cs.CV",
"cs.LG"
] |
This paper addresses a new interpretation of reinforcement learning (RL) as
reverse Kullback-Leibler (KL) divergence optimization, and derives a new
optimization method using forward KL divergence. Although RL originally aims to
maximize return indirectly through optimization of policy, the recent work by
Levine has proposed a different derivation process with explicit consideration
of optimality as stochastic variable. This paper follows this concept and
formulates the traditional learning laws for both value function and policy as
the optimization problems with reverse KL divergence including optimality.
Focusing on the asymmetry of KL divergence, the new optimization problems with
forward KL divergence are derived. Remarkably, such new optimization problems
can be regarded as optimistic RL. That optimism is intuitively specified by a
hyperparameter converted from an uncertainty parameter. In addition, it can be
enhanced when it is integrated with prioritized experience replay and
eligibility traces, both of which accelerate learning. The effects of this
expected optimism was investigated through learning tendencies on numerical
simulations using Pybullet. As a result, moderate optimism accelerated learning
and yielded higher rewards. In a realistic robotic simulation, the proposed
method with the moderate optimism outperformed one of the state-of-the-art RL
method. | [
"cs.LG"
] |
Vision-and-language pretraining (VLP) aims to learn generic multimodal
representations from massive image-text pairs. While various successful
attempts have been proposed, learning fine-grained semantic alignments between
image-text pairs plays a key role in their approaches. Nevertheless, most
existing VLP approaches have not fully utilized the intrinsic knowledge within
the image-text pairs, which limits the effectiveness of the learned alignments
and further restricts the performance of their models. To this end, we
introduce a new VLP method called ROSITA, which integrates the cross- and
intra-modal knowledge in a unified scene graph to enhance the semantic
alignments. Specifically, we introduce a novel structural knowledge masking
(SKM) strategy to use the scene graph structure as a priori to perform masked
language (region) modeling, which enhances the semantic alignments by
eliminating the interference information within and across modalities.
Extensive ablation studies and comprehensive analysis verifies the
effectiveness of ROSITA in semantic alignments. Pretrained with both in-domain
and out-of-domain datasets, ROSITA significantly outperforms existing
state-of-the-art VLP methods on three typical vision-and-language tasks over
six benchmark datasets. | [
"cs.CV",
"cs.CL"
] |
This digital book contains a practical and comprehensive introduction of
everything related to deep learning in the context of physical simulations. As
much as possible, all topics come with hands-on code examples in the form of
Jupyter notebooks to quickly get started. Beyond standard supervised learning
from data, we'll look at physical loss constraints, more tightly coupled
learning algorithms with differentiable simulations, as well as reinforcement
learning and uncertainty modeling. We live in exciting times: these methods
have a huge potential to fundamentally change what computer simulations can
achieve. | [
"cs.LG",
"physics.comp-ph"
] |
Deep learning (DL) based semantic segmentation methods have been providing
state-of-the-art performance in the last few years. More specifically, these
techniques have been successfully applied to medical image classification,
segmentation, and detection tasks. One deep learning technique, U-Net, has
become one of the most popular for these applications. In this paper, we
propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well
as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net
models, which are named RU-Net and R2U-Net respectively. The proposed models
utilize the power of U-Net, Residual Network, as well as RCNN. There are
several advantages of these proposed architectures for segmentation tasks.
First, a residual unit helps when training deep architecture. Second, feature
accumulation with recurrent residual convolutional layers ensures better
feature representation for segmentation tasks. Third, it allows us to design
better U-Net architecture with same number of network parameters with better
performance for medical image segmentation. The proposed models are tested on
three benchmark datasets such as blood vessel segmentation in retina images,
skin cancer segmentation, and lung lesion segmentation. The experimental
results show superior performance on segmentation tasks compared to equivalent
models including U-Net and residual U-Net (ResU-Net). | [
"cs.CV"
] |
Due to confidentiality issues, it can be difficult to access or share
interesting datasets for methodological development in actuarial science, or
other fields where personal data are important. We show how to design three
different types of generative adversarial networks (GANs) that can build a
synthetic insurance dataset from a confidential original dataset. The goal is
to obtain synthetic data that no longer contains sensitive information but
still has the same structure as the original dataset and retains the
multivariate relationships. In order to adequately model the specific
characteristics of insurance data, we use GAN architectures adapted for
multi-categorical data: a Wassertein GAN with gradient penalty (MC-WGAN-GP), a
conditional tabular GAN (CTGAN) and a Mixed Numerical and Categorical
Differentially Private GAN (MNCDP-GAN). For transparency, the approaches are
illustrated using a public dataset, the French motor third party liability
data. We compare the three different GANs on various aspects: ability to
reproduce the original data structure and predictive models, privacy, and ease
of use. We find that the MC-WGAN-GP synthesizes the best data, the CTGAN is the
easiest to use, and the MNCDP-GAN guarantees differential privacy. | [
"stat.ML",
"cs.LG"
] |
Active learning is a unique abstraction of machine learning techniques where
the model/algorithm could guide users for annotation of a set of data points
that would be beneficial to the model, unlike passive machine learning. The
primary advantage being that active learning frameworks select data points that
can accelerate the learning process of a model and can reduce the amount of
data needed to achieve full accuracy as compared to a model trained on a
randomly acquired data set. Multiple frameworks for active learning combined
with deep learning have been proposed, and the majority of them are dedicated
to classification tasks. Herein, we explore active learning for the task of
segmentation of medical imaging data sets. We investigate our proposed
framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of
pancreas and tumors. This work presents a query-by-committee approach for
active learning where a joint optimizer is used for the committee. At the same
time, we propose three new strategies for active learning: 1.) increasing
frequency of uncertain data to bias the training data set; 2.) Using mutual
information among the input images as a regularizer for acquisition to ensure
diversity in the training dataset; 3.) adaptation of Dice log-likelihood for
Stein variational gradient descent (SVGD). The results indicate an improvement
in terms of data reduction by achieving full accuracy while only using 22.69 %
and 48.85 % of the available data for each dataset, respectively. | [
"cs.CV"
] |
Deep neural networks (DNNs) have recently achieved great success in many
visual recognition tasks. However, existing deep neural network models are
computationally expensive and memory intensive, hindering their deployment in
devices with low memory resources or in applications with strict latency
requirements. Therefore, a natural thought is to perform model compression and
acceleration in deep networks without significantly decreasing the model
performance. During the past five years, tremendous progress has been made in
this area. In this paper, we review the recent techniques for compacting and
accelerating DNN models. In general, these techniques are divided into four
categories: parameter pruning and quantization, low-rank factorization,
transferred/compact convolutional filters, and knowledge distillation. Methods
of parameter pruning and quantization are described first, after that the other
techniques are introduced. For each category, we also provide insightful
analysis about the performance, related applications, advantages, and
drawbacks. Then we go through some very recent successful methods, for example,
dynamic capacity networks and stochastic depths networks. After that, we survey
the evaluation matrices, the main datasets used for evaluating the model
performance, and recent benchmark efforts. Finally, we conclude this paper,
discuss remaining the challenges and possible directions for future work. | [
"cs.LG",
"cs.CV"
] |
Recently the problem of cross-domain object detection has started drawing
attention in the computer vision community. In this paper, we propose a novel
unsupervised cross-domain detection model that exploits the annotated data in a
source domain to train an object detector for a different target domain. The
proposed model mitigates the cross-domain representation divergence for object
detection by performing cross-domain feature alignment in two dimensions, the
depth dimension and the spatial dimension. In the depth dimension of channel
layers, it uses inter-channel information to bridge the domain divergence with
respect to image style alignment. In the dimension of spatial layers, it
deploys spatial attention modules to enhance detection relevant regions and
suppress irrelevant regions with respect to cross-domain feature alignment.
Experiments are conducted on a number of benchmark cross-domain detection
datasets. The empirical results show the proposed method outperforms the
state-of-the-art comparison methods. | [
"cs.CV"
] |
We investigate the capability of a transformer pretrained on natural language
to generalize to other modalities with minimal finetuning -- in particular,
without finetuning of the self-attention and feedforward layers of the residual
blocks. We consider such a model, which we call a Frozen Pretrained Transformer
(FPT), and study finetuning it on a variety of sequence classification tasks
spanning numerical computation, vision, and protein fold prediction. In
contrast to prior works which investigate finetuning on the same modality as
the pretraining dataset, we show that pretraining on natural language can
improve performance and compute efficiency on non-language downstream tasks.
Additionally, we perform an analysis of the architecture, comparing the
performance of a random initialized transformer to a random LSTM. Combining the
two insights, we find language-pretrained transformers can obtain strong
performance on a variety of non-language tasks. | [
"cs.LG",
"cs.AI"
] |
Human observers can learn to recognize new categories of images from a
handful of examples, yet doing so with artificial ones remains an open
challenge. We hypothesize that data-efficient recognition is enabled by
representations which make the variability in natural signals more predictable.
We therefore revisit and improve Contrastive Predictive Coding, an unsupervised
objective for learning such representations. This new implementation produces
features which support state-of-the-art linear classification accuracy on the
ImageNet dataset. When used as input for non-linear classification with deep
neural networks, this representation allows us to use 2-5x less labels than
classifiers trained directly on image pixels. Finally, this unsupervised
representation substantially improves transfer learning to object detection on
the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet
classifiers. | [
"cs.CV",
"cs.LG"
] |
In this report, we are presenting our automated prediction system for disease
classification within dermoscopic images. The proposed solution is based on
deep learning, where we employed transfer learning strategy on VGG16 and
GoogLeNet architectures. The key feature of our solution is preprocessing based
primarily on image augmentation and colour normalization. The solution was
evaluated on Task 3: Lesion Diagnosis of the ISIC 2018: Skin Lesion Analysis
Towards Melanoma Detection. | [
"cs.CV"
] |
Video anomaly detection has gained significant attention due to the
increasing requirements of automatic monitoring for surveillance videos.
Especially, the prediction based approach is one of the most studied methods to
detect anomalies by predicting frames that include abnormal events in the test
set after learning with the normal frames of the training set. However, a lot
of prediction networks are computationally expensive owing to the use of
pre-trained optical flow networks, or fail to detect abnormal situations
because of their strong generative ability to predict even the anomalies. To
address these shortcomings, we propose spatial rotation transformation (SRT)
and temporal mixing transformation (TMT) to generate irregular patch cuboids
within normal frame cuboids in order to enhance the learning of normal
features. Additionally, the proposed patch transformation is used only during
the training phase, allowing our model to detect abnormal frames at fast speed
during inference. Our model is evaluated on three anomaly detection benchmarks,
achieving competitive accuracy and surpassing all the previous works in terms
of speed. | [
"cs.CV"
] |
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets. | [
"cs.CV"
] |
Given stereo or egomotion image pairs, a popular and successful method for
unsupervised learning of monocular depth estimation is to measure the quality
of image reconstructions resulting from the learned depth predictions.
Continued research has improved the overall approach in recent years, yet the
common framework still suffers from several important limitations, particularly
when dealing with points occluded after transformation to a novel viewpoint.
While prior work has addressed this problem heuristically, this paper
introduces a z-buffering algorithm that correctly and efficiently handles
occluded points. Because our algorithm is implemented with operators typical of
machine learning libraries, it can be incorporated into any existing
unsupervised depth learning framework with automatic support for
differentiation. Additionally, because points having negative depth after
transformation often signify erroneously shallow depth predictions, we
introduce a loss function to penalize this undesirable behavior explicitly.
Experimental results on the KITTI data set show that the z-buffer and negative
depth loss both improve the performance of a state of the art depth-prediction
network. | [
"cs.CV",
"cs.RO"
] |
Graph neural networks (GNNs) are popular to use for classifying structured
data in the context of machine learning. But surprisingly, they are rarely
applied to regression problems. In this work, we adopt GNN for a classic but
challenging nonlinear regression problem, namely the network localization. Our
main findings are in order. First, GNN is potentially the best solution to
large-scale network localization in terms of accuracy, robustness and
computational time. Second, proper thresholding of the communication range is
essential to its superior performance. Simulation results corroborate that the
proposed GNN based method outperforms all state-of-the-art benchmarks by far.
Such inspiring results are theoretically justified in terms of data
aggregation, non-line-of-sight (NLOS) noise removal and low-pass filtering
effect, all affected by the threshold for neighbor selection. Code is available
at https://github.com/Yanzongzi/GNN-For-localization. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
Supervised deep convolutional neural networks (DCNNs) are currently one of
the best computational models that can explain how the primate ventral visual
stream solves object recognition. However, embodied cognition has not been
considered in the existing visual processing models. From the ecological
standpoint, humans learn to recognize objects by interacting with them,
allowing better classification, specialization, and generalization. Here, we
ask if computational models under the embodied learning framework can explain
mechanisms underlying object recognition in the primate visual system better
than the existing supervised models? To address this question, we use
reinforcement learning to train neural network models to play a 3D computer
game and we find that these reinforcement learning models achieve neural
response prediction accuracy scores in the early visual areas (e.g., V1 and V2)
in the levels that are comparable to those accomplished by the supervised
neural network model. In contrast, the supervised neural network models yield
better neural response predictions in the higher visual areas, compared to the
reinforcement learning models. Our preliminary results suggest the future
direction of visual neuroscience in which deep reinforcement learning should be
included to fill the missing embodiment concept. | [
"cs.LG",
"q-bio.NC"
] |
We address the problem of learning binary decision trees that partition data
for some downstream task. We propose to learn discrete parameters (i.e., for
tree traversals and node pruning) and continuous parameters (i.e., for tree
split functions and prediction functions) simultaneously using argmin
differentiation. We do so by sparsely relaxing a mixed-integer program for the
discrete parameters, to allow gradients to pass through the program to
continuous parameters. We derive customized algorithms to efficiently compute
the forward and backward passes. This means that our tree learning procedure
can be used as an (implicit) layer in arbitrary deep networks, and can be
optimized with arbitrary loss functions. We demonstrate that our approach
produces binary trees that are competitive with existing single tree and
ensemble approaches, in both supervised and unsupervised settings. Further,
apart from greedy approaches (which do not have competitive accuracies), our
method is faster to train than all other tree-learning baselines we compare
with. The code for reproducing the results is available at
https://github.com/vzantedeschi/LatentTrees. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Multi-focus image fusion technologies compress different focus depth images
into an image in which most objects are in focus. However, although existing
image fusion techniques, including traditional algorithms and deep
learning-based algorithms, can generate high-quality fused images, they need
multiple images with different focus depths in the same field of view. This
criterion may not be met in some cases where time efficiency is required or the
hardware is insufficient. The problem is especially prominent in large-size
whole slide images. This paper focused on the multi-focus image fusion of
cytopathological digital slide images, and proposed a novel method for
generating fused images from single-focus or few-focus images based on
conditional generative adversarial network (GAN). Through the adversarial
learning of the generator and discriminator, the method is capable of
generating fused images with clear textures and large depth of field. Combined
with the characteristics of cytopathological images, this paper designs a new
generator architecture combining U-Net and DenseBlock, which can effectively
improve the network's receptive field and comprehensively encode image
features. Meanwhile, this paper develops a semantic segmentation network that
identifies the blurred regions in cytopathological images. By integrating the
network into the generative model, the quality of the generated fused images is
effectively improved. Our method can generate fused images from only
single-focus or few-focus images, thereby avoiding the problem of collecting
multiple images of different focus depths with increased time and hardware
costs. Furthermore, our model is designed to learn the direct mapping of input
source images to fused images without the need to manually design complex
activity level measurements and fusion rules as in traditional methods. | [
"cs.CV",
"eess.IV",
"q-bio.QM"
] |
Learning depth from spherical panoramas is becoming a popular research topic
because a panorama has a full field-of-view of the environment and provides a
relatively complete description of a scene. However, applying well-studied CNNs
for perspective images to the standard representation of spherical panoramas,
i.e., the equirectangular projection, is suboptimal, as it becomes distorted
towards the poles. Another representation is the cubemap projection, which is
distortion-free but discontinued on edges and limited in the field-of-view.
This paper introduces a new framework to fuse features from the two
projections, unidirectionally feeding the cubemap features to the
equirectangular features only at the decoding stage. Unlike the recent
bidirectional fusion approach operating at both the encoding and decoding
stages, our fusion scheme is much more efficient. Besides, we also designed a
more effective fusion module for our fusion scheme. Experiments verify the
effectiveness of our proposed fusion strategy and module, and our model
achieves state-of-the-art performance on four popular datasets. Additional
experiments show that our model also has the advantages of model complexity and
generalization capability.The code is available at
https://github.com/alibaba/UniFuse-Unidirectional-Fusion. | [
"cs.CV",
"cs.RO"
] |
Ride-hailing platforms generally provide various service options to
customers, such as solo ride services, shared ride services, etc. It is
generally expected that demands for different service modes are correlated, and
the prediction of demand for one service mode can benefit from historical
observations of demands for other service modes. Moreover, an accurate joint
prediction of demands for multiple service modes can help the platforms better
allocate and dispatch vehicle resources. Although there is a large stream of
literature on ride-hailing demand predictions for one specific service mode,
little efforts have been paid towards joint predictions of ride-hailing demands
for multiple service modes. To address this issue, we propose a deep multi-task
multi-graph learning approach, which combines two components: (1) multiple
multi-graph convolutional (MGC) networks for predicting demands for different
service modes, and (2) multi-task learning modules that enable knowledge
sharing across multiple MGC networks. More specifically, two multi-task
learning structures are established. The first one is the regularized
cross-task learning, which builds cross-task connections among the inputs and
outputs of multiple MGC networks. The second one is the multi-linear
relationship learning, which imposes a prior tensor normal distribution on the
weights of various MGC networks. Although there are no concrete bridges between
different MGC networks, the weights of these networks are constrained by each
other and subject to a common prior distribution. Evaluated with the
for-hire-vehicle datasets in Manhattan, we show that our propose approach
outperforms the benchmark algorithms in prediction accuracy for different
ride-hailing modes. | [
"cs.LG",
"cs.AI"
] |
This paper proposes combining spatio-temporal appearance (STA) descriptors
with optical flow for human action recognition. The STA descriptors are local
histogram-based descriptors of space-time, suitable for building a partial
representation of arbitrary spatio-temporal phenomena. Because of the
possibility of iterative refinement, they are interesting in the context of
online human action recognition. We investigate the use of dense optical flow
as the image function of the STA descriptor for human action recognition, using
two different algorithms for computing the flow: the Farneb\"ack algorithm and
the TVL1 algorithm. We provide a detailed analysis of the influencing optical
flow algorithm parameters on the produced optical flow fields. An extensive
experimental validation of optical flow-based STA descriptors in human action
recognition is performed on the KTH human action dataset. The encouraging
experimental results suggest the potential of our approach in online human
action recognition. | [
"cs.CV"
] |
Policy-gradient approaches to reinforcement learning have two common and
undesirable overhead procedures, namely warm-start training and sample variance
reduction. In this paper, we describe a reinforcement learning method based on
a softmax value function that requires neither of these procedures. Our method
combines the advantages of policy-gradient methods with the efficiency and
simplicity of maximum-likelihood approaches. We apply this new cold-start
reinforcement learning method in training sequence generation models for
structured output prediction problems. Empirical evidence validates this method
on automatic summarization and image captioning tasks. | [
"cs.LG"
] |
Deep neural networks have shown promising results for various clinical
prediction tasks such as diagnosis, mortality prediction, predicting duration
of stay in hospital, etc. However, training deep networks -- such as those
based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high
computational resources, and significant hyperparameter tuning effort. In this
work, we investigate as to what extent can transfer learning address these
issues when using deep RNNs to model multivariate clinical time series. We
consider transferring the knowledge captured in an RNN trained on several
source tasks simultaneously using a large labeled dataset to build the model
for a target task with limited labeled data. An RNN pre-trained on several
tasks provides generic features, which are then used to build simpler linear
models for new target tasks without training task-specific RNNs. For
evaluation, we train a deep RNN to identify several patient phenotypes on time
series from MIMIC-III database, and then use the features extracted using that
RNN to build classifiers for identifying previously unseen phenotypes, and also
for a seemingly unrelated task of in-hospital mortality. We demonstrate that
(i) models trained on features extracted using pre-trained RNN outperform or,
in the worst case, perform as well as task-specific RNNs; (ii) the models using
features from pre-trained models are more robust to the size of labeled data
than task-specific RNNs; and (iii) features extracted using pre-trained RNN are
generic enough and perform better than typical statistical hand-crafted
features. | [
"cs.LG",
"stat.ML"
] |
The remarkable advancements in Deep Learning (DL) algorithms have fueled
enthusiasm for using Artificial Intelligence (AI) technologies in almost every
domain; however, the opaqueness of these algorithms put a question mark on
their applications in safety-critical systems. In this regard, the
`explainability' dimension is not only essential to both explain the inner
workings of black-box algorithms, but it also adds accountability and
transparency dimensions that are of prime importance for regulators, consumers,
and service providers. eXplainable Artificial Intelligence (XAI) is the set of
techniques and methods to convert the so-called black-box AI algorithms to
white-box algorithms, where the results achieved by these algorithms and the
variables, parameters, and steps taken by the algorithm to reach the obtained
results, are transparent and explainable. To complement the existing literature
on XAI, in this paper, we take an `engineering' approach to illustrate the
concepts of XAI. We discuss the stakeholders in XAI and describe the
mathematical contours of XAI from engineering perspective. Then we take the
autonomous car as a use-case and discuss the applications of XAI for its
different components such as object detection, perception, control, action
decision, and so on. This work is an exploratory study to identify new avenues
of research in the field of XAI. | [
"cs.LG",
"cs.AI"
] |
Convolutional neural networks have been applied to a wide variety of computer
vision tasks. Recent advances in semantic segmentation have enabled their
application to medical image segmentation. While most CNNs use two-dimensional
kernels, recent CNN-based publications on medical image segmentation featured
three-dimensional kernels, allowing full access to the three-dimensional
structure of medical images. Though closely related to semantic segmentation,
medical image segmentation includes specific challenges that need to be
addressed, such as the scarcity of labelled data, the high class imbalance
found in the ground truth and the high memory demand of three-dimensional
images. In this work, a CNN-based method with three-dimensional filters is
demonstrated and applied to hand and brain MRI. Two modifications to an
existing CNN architecture are discussed, along with methods on addressing the
aforementioned challenges. While most of the existing literature on medical
image segmentation focuses on soft tissue and the major organs, this work is
validated on data both from the central nervous system as well as the bones of
the hand. | [
"cs.CV"
] |
Ensembles of decision trees perform well on many problems, but are not
interpretable. In contrast to existing approaches in interpretability that
focus on explaining relationships between features and predictions, we propose
an alternative approach to interpret tree ensemble classifiers by surfacing
representative points for each class -- prototypes. We introduce a new distance
for Gradient Boosted Tree models, and propose new, adaptive prototype selection
methods with theoretical guarantees, with the flexibility to choose a different
number of prototypes in each class. We demonstrate our methods on random
forests and gradient boosted trees, showing that the prototypes can perform as
well as or even better than the original tree ensemble when used as a
nearest-prototype classifier. In a user study, humans were better at predicting
the output of a tree ensemble classifier when using prototypes than when using
Shapley values, a popular feature attribution method. Hence, prototypes present
a viable alternative to feature-based explanations for tree ensembles. | [
"stat.ML",
"cs.LG"
] |
Within many real-world networks the links between pairs of nodes change over
time. Thus, there has been a recent boom in studying temporal graphs.
Recognizing patterns in temporal graphs requires a proximity measure to compare
different temporal graphs. To this end, we propose to study dynamic time
warping on temporal graphs. We define the dynamic temporal graph warping
distance (dtgw) to determine the dissimilarity of two temporal graphs. Our
novel measure is flexible and can be applied in various application domains. We
show that computing the dtgw-distance is a challenging (in general) NP-hard
optimization problem and identify some polynomial-time solvable special cases.
Moreover, we develop a quadratic programming formulation and an efficient
heuristic. In experiments on real-word data we show that the heuristic performs
very well and that our dtgw-distance performs favorably in de-anonymizing
networks compared to other approaches. | [
"cs.LG",
"stat.ML",
"F.2.2; G.2.2"
] |
Many approaches for estimation of Remaining Useful Life (RUL) of a machine,
using its operational sensor data, make assumptions about how a system degrades
or a fault evolves, e.g., exponential degradation. However, in many domains
degradation may not follow a pattern. We propose a Long Short Term Memory based
Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI)
for a system using multi-sensor time-series data. LSTM-ED is trained to
reconstruct the time-series corresponding to healthy state of a system. The
reconstruction error is used to compute HI which is then used for RUL
estimation. We evaluate our approach on publicly available Turbofan Engine and
Milling Machine datasets. We also present results on a real-world industry
dataset from a pulverizer mill where we find significant correlation between
LSTM-ED based HI and maintenance costs. | [
"cs.LG",
"cs.AI"
] |
Long-range context information is crucial for the semantic segmentation of
High-Resolution (HR) Remote Sensing Images (RSIs). The image cropping
operations, commonly used for training neural networks, limit the perception of
long-range context information in large RSIs. To break this limitation, we
propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR
RSIs. In the WiCoNet, apart from a conventional feature extraction network that
aggregates the local information, an extra context branch is designed to
explicitly model the spatial information in a larger image area. The
information between the two branches is communicated through a Context
Transformer, which is a novel design derived from the Vision Transformer to
model the long-range context correlations. Ablation studies and comparative
experiments conducted on several benchmark datasets prove the effectiveness of
the proposed method. In addition, we present a new Beijing Land-Use (BLU)
dataset. This is a large-scale HR satellite dataset provided with high-quality
and fine-grained reference labels, which can boost future studies in this
field. | [
"cs.CV"
] |
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