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The recent researches in Deep Convolutional Neural Network have focused their
attention on improving accuracy that provide significant advances. However, if
they were limited to classification tasks, nowadays with contributions from
Scientific Communities who are embarking in this field, they have become very
useful in higher level tasks such as object detection and pixel-wise semantic
segmentation. Thus, brilliant ideas in the field of semantic segmentation with
deep learning have completed the state of the art of accuracy, however this
architectures become very difficult to apply in embedded systems as is the case
for autonomous driving. We present a new Deep fully Convolutional Neural
Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The
architecture is based on Encoder-Decoder style. We use a SqueezeNet-like
encoder and a decoder formed by our proposed squeeze-decoder module and
upsample layer using downsample indices like in SegNet and we add a
deconvolution layer to provide final multi-channel feature map. On datasets
like Camvid or City-states, our net gets SegNet-level accuracy with less than
10 times fewer parameters than SegNet. | [
"cs.CV"
] |
Deep neural networks have improved image classification dramatically over the
past decade, but have done so by focusing on performance measures that treat
all classes other than the ground truth as equally wrong. This has led to a
situation in which mistakes are less likely to be made than before, but are
equally likely to be absurd or catastrophic when they do occur. Past works have
recognised and tried to address this issue of mistake severity, often by using
graph distances in class hierarchies, but this has largely been neglected since
the advent of the current deep learning era in computer vision. In this paper,
we aim to renew interest in this problem by reviewing past approaches and
proposing two simple modifications of the cross-entropy loss which outperform
the prior art under several metrics on two large datasets with complex class
hierarchies: tieredImageNet and iNaturalist'19. | [
"cs.CV",
"cs.LG"
] |
Semantic understanding of scenes in three-dimensional space (3D) is a
quintessential part of robotics oriented applications such as autonomous
driving as it provides geometric cues such as size, orientation and true
distance of separation to objects which are crucial for taking mission critical
decisions. As a first step, in this work we investigate the possibility of
semantically classifying different parts of a given scene in 3D by learning the
underlying geometric context in addition to the texture cues BUT in the absence
of labelled real-world datasets. To this end we generate a large number of
synthetic scenes, their pixel-wise labels and corresponding 3D representations
using CARLA software framework. We then build a deep neural network that learns
underlying category specific 3D representation and texture cues from color
information of the rendered synthetic scenes. Further on we apply the learned
model on different real world datasets to evaluate its performance. Our
preliminary investigation of results show that the neural network is able to
learn the geometric context from synthetic scenes and effectively apply this
knowledge to classify each point of a 3D representation of a scene in
real-world. | [
"cs.CV",
"cs.AI",
"I.4"
] |
Training with more data has always been the most stable and effective way of
improving performance in deep learning era. As the largest object detection
dataset so far, Open Images brings great opportunities and challenges for
object detection in general and sophisticated scenarios. However, owing to its
semi-automatic collecting and labeling pipeline to deal with the huge data
scale, Open Images dataset suffers from label-related problems that objects may
explicitly or implicitly have multiple labels and the label distribution is
extremely imbalanced. In this work, we quantitatively analyze these label
problems and provide a simple but effective solution. We design a concurrent
softmax to handle the multi-label problems in object detection and propose a
soft-sampling methods with hybrid training scheduler to deal with the label
imbalance. Overall, our method yields a dramatic improvement of 3.34 points,
leading to the best single model with 60.90 mAP on the public object detection
test set of Open Images. And our ensembling result achieves 67.17 mAP, which is
4.29 points higher than the best result of Open Images public test 2018. | [
"cs.CV"
] |
3D object detection is receiving increasing attention from both industry and
academia thanks to its wide applications in various fields. In this paper, we
propose the Point-Voxel Region based Convolution Neural Networks (PV-RCNNs) for
accurate 3D detection from point clouds. First, we propose a novel 3D object
detector, PV-RCNN-v1, which employs the voxel-to-keypoint scene encoding and
keypoint-to-grid RoI feature abstraction two novel steps. These two steps
deeply incorporate both 3D voxel CNN and PointNet-based set abstraction for
learning discriminative point-cloud features. Second, we propose a more
advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection.
It consists of two major improvements, where the first one is the sectorized
proposal-centric strategy for efficiently producing more representative and
uniformly distributed keypoints, and the second one is the VectorPool
aggregation to replace set abstraction for better aggregating local point-cloud
features with much less resource consumption. With these two major
modifications, our PV-RCNN-v2 runs more than twice as fast as the v1 version
while still achieving better performance on the large-scale Waymo Open Dataset
with 150m * 150m detection range. Extensive experiments demonstrate that our
proposed PV-RCNNs significantly outperform previous state-of-the-art 3D
detection methods on both the Waymo Open Dataset and the highly-competitive
KITTI benchmark. | [
"cs.CV"
] |
Actin cytoskeleton networks generate local topological signatures due to the
natural variations in the number, size, and shape of holes of the networks.
Persistent homology is a method that explores these topological properties of
data and summarizes them as persistence diagrams. In this work, we analyze and
classify these filament networks by transforming them into persistence diagrams
whose variability is quantified via a Bayesian framework on the space of
persistence diagrams. The proposed generalized Bayesian framework adopts an
independent and identically distributed cluster point process characterization
of persistence diagrams and relies on a substitution likelihood argument. This
framework provides the flexibility to estimate the posterior cardinality
distribution of points in a persistence diagram and the posterior spatial
distribution simultaneously. We present a closed form of the posteriors under
the assumption of Gaussian mixtures and binomials for prior intensity and
cardinality respectively. Using this posterior calculation, we implement a
Bayes factor algorithm to classify the actin filament networks and benchmark it
against several state-of-the-art classification methods. | [
"stat.ML",
"cs.LG",
"q-bio.QM",
"62F15, 60G55, 62-07, and 62P10"
] |
Sign Languages are rich multi-channel languages, requiring articulation of
both manual (hands) and non-manual (face and body) features in a precise,
intricate manner. Sign Language Production (SLP), the automatic translation
from spoken to sign languages, must embody this full sign morphology to be
truly understandable by the Deaf community. Previous work has mainly focused on
manual feature production, with an under-articulated output caused by
regression to the mean.
In this paper, we propose an Adversarial Multi-Channel approach to SLP. We
frame sign production as a minimax game between a transformer-based Generator
and a conditional Discriminator. Our adversarial discriminator evaluates the
realism of sign production conditioned on the source text, pushing the
generator towards a realistic and articulate output. Additionally, we fully
encapsulate sign articulators with the inclusion of non-manual features,
producing facial features and mouthing patterns.
We evaluate on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T)
dataset, and report state-of-the art SLP back-translation performance for
manual production. We set new benchmarks for the production of multi-channel
sign to underpin future research into realistic SLP. | [
"cs.CV"
] |
Nowadays, many network representation learning algorithms and downstream
network mining tasks have already paid attention to dynamic networks or
temporal networks, which are more suitable for real-world complex scenarios by
modeling evolving patterns and temporal dependencies between node interactions.
Moreover, representing and mining temporal networks have a wide range of
applications, such as fraud detection, social network analysis, and drug
discovery. To contribute to the network representation learning and network
mining research community, in this paper, we generate a new biological
repository of dynamic protein-protein interaction network data (i.e., DPPIN),
which consists of twelve dynamic network datasets describing protein-level
interactions of yeast cells at different scales. We first introduce the
generation process of DPPIN. To demonstrate the value of our published
repository DPPIN, we then list the potential applications that would be
benefited. Furthermore, we design dynamic local clustering, dynamic spectral
clustering, dynamic subgraph matching, dynamic node classification, and dynamic
graph classification experiments, where network datasets of DPPIN could
indicate future research opportunities for some tasks by presenting challenges
on state-of-the-art baseline algorithms. Finally, we identify future directions
for improving the utility of this repository and welcome constructive inputs
from the community. All resources of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN. | [
"cs.LG"
] |
Automatic event detection from time series signals has wide applications,
such as abnormal event detection in video surveillance and event detection in
geophysical data. Traditional detection methods detect events primarily by the
use of similarity and correlation in data. Those methods can be inefficient and
yield low accuracy. In recent years, because of the significantly increased
computational power, machine learning techniques have revolutionized many
science and engineering domains. In this study, we apply a deep-learning-based
method to the detection of events from time series seismic signals. However, a
direct adaptation of the similar ideas from 2D object detection to our problem
faces two challenges. The first challenge is that the duration of earthquake
event varies significantly; The other is that the proposals generated are
temporally correlated. To address these challenges, we propose a novel cascaded
region-based convolutional neural network to capture earthquake events in
different sizes, while incorporating contextual information to enrich features
for each individual proposal. To achieve a better generalization performance,
we use densely connected blocks as the backbone of our network. Because of the
fact that some positive events are not correctly annotated, we further
formulate the detection problem as a learning-from-noise problem. To verify the
performance of our detection methods, we employ our methods to seismic data
generated from a bi-axial "earthquake machine" located at Rock Mechanics
Laboratory, and we acquire labels with the help of experts. Through our
numerical tests, we show that our novel detection techniques yield high
accuracy. Therefore, our novel deep-learning-based detection methods can
potentially be powerful tools for locating events from time series data in
various applications. | [
"cs.LG",
"cs.CV"
] |
Generative adversarial networks (GANs) are a powerful approach to
unsupervised learning. They have achieved state-of-the-art performance in the
image domain. However, GANs are limited in two ways. They often learn
distributions with low support---a phenomenon known as mode collapse---and they
do not guarantee the existence of a probability density, which makes evaluating
generalization using predictive log-likelihood impossible. In this paper, we
develop the prescribed GAN (PresGAN) to address these shortcomings. PresGANs
add noise to the output of a density network and optimize an
entropy-regularized adversarial loss. The added noise renders tractable
approximations of the predictive log-likelihood and stabilizes the training
procedure. The entropy regularizer encourages PresGANs to capture all the modes
of the data distribution. Fitting PresGANs involves computing the intractable
gradients of the entropy regularization term; PresGANs sidestep this
intractability using unbiased stochastic estimates. We evaluate PresGANs on
several datasets and found they mitigate mode collapse and generate samples
with high perceptual quality. We further found that PresGANs reduce the gap in
performance in terms of predictive log-likelihood between traditional GANs and
variational autoencoders (VAEs). | [
"stat.ML",
"cs.LG",
"stat.ME"
] |
With the vast amount of data collected on football and the growth of
computing abilities, many games involving decision choices can be optimized.
The underlying rule is the maximization of an expected utility of outcomes and
the law of large numbers. The data available allows us to compute with high
accuracy the probabilities of outcomes of decisions and the well defined points
system in the game allows us to have the necessary terminal utilities. With
some well established theory we can then optimize choices at a single play
level. | [
"cs.LG",
"stat.AP",
"stat.ME",
"stat.ML"
] |
Recent advances in deep pose estimation models have proven to be effective in
a wide range of applications such as health monitoring, sports, animations, and
robotics. However, pose estimation models fail to generalize when facing images
acquired from in-bed pressure sensing systems. In this paper, we address this
challenge by presenting a novel end-to-end framework capable of accurately
locating body parts from vague pressure data. Our method exploits the idea of
equipping an off-the-shelf pose estimator with a deep trainable neural network,
which pre-processes and prepares the pressure data for subsequent pose
estimation. Our model transforms the ambiguous pressure maps to images
containing shapes and structures similar to the common input domain of the
pre-existing pose estimation methods. As a result, we show that our model is
able to reconstruct unclear body parts, which in turn enables pose estimators
to accurately and robustly estimate the pose. We train and test our method on a
manually annotated public pressure map dataset using a combination of loss
functions. Results confirm the effectiveness of our method by the high visual
quality in the generated images and the high pose estimation rates achieved. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Active search is the process of identifying high-value data points in a large
and often high-dimensional parameter space that can be expensive to evaluate.
Traditional active search techniques like Bayesian optimization trade off
exploration and exploitation over consecutive evaluations, and have
historically focused on single or small (<5) numbers of examples evaluated per
round. As modern data sets grow, so does the need to scale active search to
large data sets and batch sizes. In this paper, we present a general
hierarchical framework based on bandit algorithms to scale active search to
large batch sizes by maximizing information derived from the unique structure
of each dataset. Our hierarchical framework, Hierarchical Batch Bandit Search
(HBBS), strategically distributes batch selection across a learned embedding
space by facilitating wide exploration of different structural elements within
a dataset. We focus our application of HBBS on modern biology, where large
batch experimentation is often fundamental to the research process, and
demonstrate batch design of biological sequences (protein and DNA). We also
present a new Gym environment to easily simulate diverse biological sequences
and to enable more comprehensive evaluation of active search methods across
heterogeneous data sets. The HBBS framework improves upon standard performance,
wall-clock, and scalability benchmarks for batch search by using a broad
exploration strategy across coarse partitions and fine-grained exploitation
within each partition of structured data. | [
"cs.LG",
"q-bio.QM",
"stat.ML"
] |
Explainability is becoming an important requirement for organizations that
make use of automated decision-making due to regulatory initiatives and a shift
in public awareness. Various and significantly different algorithmic methods to
provide this explainability have been introduced in the field, but the existing
literature in the machine learning community has paid little attention to the
stakeholder whose needs are rather studied in the human-computer interface
community. Therefore, organizations that want or need to provide this
explainability are confronted with the selection of an appropriate method for
their use case. In this paper, we argue there is a need for a methodology to
bridge the gap between stakeholder needs and explanation methods. We present
our ongoing work on creating this methodology to help data scientists in the
process of providing explainability to stakeholders. In particular, our
contributions include documents used to characterize XAI methods and user
requirements (shown in Appendix), which our methodology builds upon. | [
"cs.LG",
"cs.AI",
"cs.CY"
] |
We describe a non-parametric, "example-based" method for estimating the depth
of an object, viewed in a single photo. Our method consults a database of
example 3D geometries, searching for those which look similar to the object in
the photo. The known depths of the selected database objects act as shape
priors which constrain the process of estimating the object's depth. We show
how this process can be performed by optimizing a well defined target
likelihood function, via a hard-EM procedure. We address the problem of
representing the (possibly infinite) variability of viewing conditions with a
finite (and often very small) example set, by proposing an on-the-fly example
update scheme. We further demonstrate the importance of non-stationarity in
avoiding misleading examples when estimating structured shapes. We evaluate our
method and present both qualitative as well as quantitative results for
challenging object classes. Finally, we show how this same technique may be
readily applied to a number of related problems. These include the novel task
of estimating the occluded depth of an object's backside and the task of
tailoring custom fitting image-maps for input depths. | [
"cs.CV",
"68T45"
] |
Graph representation learning has attracted a surge of interest recently,
whose target at learning discriminant embedding for each node in the graph.
Most of these representation methods focus on supervised learning and heavily
depend on label information. However, annotating graphs are expensive to obtain
in the real world, especially in specialized domains (i.e. biology), as it
needs the annotator to have the domain knowledge to label the graph. To
approach this problem, self-supervised learning provides a feasible solution
for graph representation learning. In this paper, we propose a Multi-Level
Graph Contrastive Learning (MLGCL) framework for learning robust representation
of graph data by contrasting space views of graphs. Specifically, we introduce
a novel contrastive view - topological and feature space views. The original
graph is first-order approximation structure and contains uncertainty or error,
while the $k$NN graph generated by encoding features preserves high-order
proximity. Thus $k$NN graph generated by encoding features not only provide a
complementary view, but is more suitable to GNN encoder to extract discriminant
representation. Furthermore, we develop a multi-level contrastive mode to
preserve the local similarity and semantic similarity of graph-structured data
simultaneously. Extensive experiments indicate MLGCL achieves promising results
compared with the existing state-of-the-art graph representation learning
methods on seven datasets. | [
"cs.LG",
"cs.AI"
] |
While current approaches for neural network training often aim at improving
performance, less focus is put on training methods aiming at robustness towards
varying noise conditions or directed attacks by adversarial examples. In this
paper, we propose to improve robustness by a multi-task training, which extends
supervised semantic segmentation by a self-supervised monocular depth
estimation on unlabeled videos. This additional task is only performed during
training to improve the semantic segmentation model's robustness at test time
under several input perturbations. Moreover, we even find that our joint
training approach also improves the performance of the model on the original
(supervised) semantic segmentation task. Our evaluation exhibits a particular
novelty in that it allows to mutually compare the effect of input noises and
adversarial attacks on the robustness of the semantic segmentation. We show the
effectiveness of our method on the Cityscapes dataset, where our multi-task
training approach consistently outperforms the single-task semantic
segmentation baseline in terms of both robustness vs. noise and in terms of
adversarial attacks, without the need for depth labels in training. | [
"cs.CV"
] |
Efforts are underway to study ways via which the power of deep neural
networks can be extended to non-standard data types such as structured data
(e.g., graphs) or manifold-valued data (e.g., unit vectors or special
matrices). Often, sizable empirical improvements are possible when the geometry
of such data spaces are incorporated into the design of the model,
architecture, and the algorithms. Motivated by neuroimaging applications, we
study formulations where the data are {\em sequential manifold-valued
measurements}. This case is common in brain imaging, where the samples
correspond to symmetric positive definite matrices or orientation distribution
functions. Instead of a recurrent model which poses computational/technical
issues, and inspired by recent results showing the viability of dilated
convolutional models for sequence prediction, we develop a dilated
convolutional neural network architecture for this task. On the technical side,
we show how the modules needed in our network can be derived while explicitly
taking the Riemannian manifold structure into account. We show how the
operations needed can leverage known results for calculating the weighted
Fr\'{e}chet Mean (wFM). Finally, we present scientific results for group
difference analysis in Alzheimer's disease (AD) where the groups are derived
using AD pathology load: here the model finds several brain fiber bundles that
are related to AD even when the subjects are all still cognitively healthy. | [
"cs.CV",
"cs.LG"
] |
Real-time detection of objects in the 3D scene is one of the tasks an
autonomous agent needs to perform for understanding its surroundings. While
recent Deep Learning-based solutions achieve satisfactory performance, their
high computational cost renders their application in real-life settings in
which computations need to be performed on embedded platforms intractable. In
this paper, we analyze the efficiency of two popular voxel-based 3D object
detection methods providing a good compromise between high performance and
speed based on two aspects, their ability to detect objects located at large
distances from the agent and their ability to operate in real time on embedded
platforms equipped with high-performance GPUs. Our experiments show that these
methods mostly fail to detect distant small objects due to the sparsity of the
input point clouds at large distances. Moreover, models trained on near objects
achieve similar or better performance compared to those trained on all objects
in the scene. This means that the models learn object appearance
representations mostly from near objects. Our findings suggest that a
considerable part of the computations of existing methods is focused on
locations of the scene that do not contribute with successful detection. This
means that the methods can achieve a speed-up of $40$-$60\%$ by restricting
operation to near objects while not sacrificing much in performance. | [
"cs.CV"
] |
Current geometry-based monocular 3D object detection models can efficiently
detect objects by leveraging perspective geometry, but their performance is
limited due to the absence of accurate depth information. Though this issue can
be alleviated in a depth-based model where a depth estimation module is plugged
to predict depth information before 3D box reasoning, the introduction of such
module dramatically reduces the detection speed. Instead of training a costly
depth estimator, we propose a rendering module to augment the training data by
synthesizing images with virtual-depths. The rendering module takes as input
the RGB image and its corresponding sparse depth image, outputs a variety of
photo-realistic synthetic images, from which the detection model can learn more
discriminative features to adapt to the depth changes of the objects. Besides,
we introduce an auxiliary module to improve the detection model by jointly
optimizing it through a depth estimation task. Both modules are working in the
training time and no extra computation will be introduced to the detection
model. Experiments show that by working with our proposed modules, a
geometry-based model can represent the leading accuracy on the KITTI 3D
detection benchmark. | [
"cs.CV"
] |
Instance segmentation has gained recently huge attention in various computer
vision applications. It aims at providing different IDs to different objects of
the scene, even if they belong to the same class. Instance segmentation is
usually performed as a two-stage pipeline. First, an object is detected, then
semantic segmentation within the detected box area is performed which involves
costly up-sampling. In this paper, we propose Insta-YOLO, a novel one-stage
end-to-end deep learning model for real-time instance segmentation. Instead of
pixel-wise prediction, our model predicts instances as object contours
represented by 2D points in Cartesian space. We evaluate our model on three
datasets, namely, Carvana,Cityscapes and Airbus. We compare our results to the
state-of-the-art models for instance segmentation. The results show our model
achieves competitive accuracy in terms of mAP at twice the speed on GTX-1080
GPU. | [
"cs.CV",
"cs.LG"
] |
In this work, we propose a novel two-stage framework for the efficient 3D
point cloud object detection. Instead of transforming point clouds into 2D bird
eye view projections, we parse the raw point cloud data directly in the 3D
space yet achieve impressive efficiency and accuracy. To achieve this goal, we
propose dynamic voxelization, a method that voxellizes points at local scale
on-the-fly. By doing so, we preserve the point cloud geometry with 3D voxels,
and therefore waive the dependence on expensive MLPs to learn from point
coordinates. On the other hand, we inherently still follow the same processing
pattern as point-wise methods (e.g., PointNet) and no longer suffer from the
quantization issue like conventional convolutions. For further speed
optimization, we propose the grid-based downsampling and voxelization method,
and provide different CUDA implementations to accommodate to the discrepant
requirements during training and inference phases. We highlight our efficiency
on KITTI 3D object detection dataset with 75 FPS and on Waymo Open dataset with
25 FPS inference speed with satisfactory accuracy. | [
"cs.CV"
] |
Class-conditional extensions of generative adversarial networks (GANs), such
as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered
attention owing to their ability to decompose representations into class labels
and other factors and to boost the training stability. However, a limitation is
that they assume that each class is separable and ignore the relationship
between classes even though class overlapping frequently occurs in a real-world
scenario when data are collected on the basis of diverse or ambiguous criteria.
To overcome this limitation, we address a novel problem called class-distinct
and class-mutual image generation, in which the goal is to construct a
generator that can capture between-class relationships and generate an image
selectively conditioned on the class specificity. To solve this problem without
additional supervision, we propose classifier's posterior GAN (CP-GAN), in
which we redesign the generator input and the objective function of AC-GAN for
class-overlapping data. Precisely, we incorporate the classifier's posterior
into the generator input and optimize the generator so that the classifier's
posterior of generated data corresponds with that of real data. We demonstrate
the effectiveness of CP-GAN using both controlled and real-world
class-overlapping data with a model configuration analysis and comparative
study. Our code is available at https://github.com/takuhirok/CP-GAN/. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Machine Learning (ML) models trained on data from multiple demographic groups
can inherit representation disparity (Hashimoto et al., 2018) that may exist in
the data: the model may be less favorable to groups contributing less to the
training process; this in turn can degrade population retention in these groups
over time, and exacerbate representation disparity in the long run. In this
study, we seek to understand the interplay between ML decisions and the
underlying group representation, how they evolve in a sequential framework, and
how the use of fairness criteria plays a role in this process. We show that the
representation disparity can easily worsen over time under a natural user
dynamics (arrival and departure) model when decisions are made based on a
commonly used objective and fairness criteria, resulting in some groups
diminishing entirely from the sample pool in the long run. It highlights the
fact that fairness criteria have to be defined while taking into consideration
the impact of decisions on user dynamics. Toward this end, we explain how a
proper fairness criterion can be selected based on a general user dynamics
model. | [
"cs.LG",
"stat.ML"
] |
Gaussian processes are flexible function approximators, with inductive biases
controlled by a covariance kernel. Learning the kernel is the key to
representation learning and strong predictive performance. In this paper, we
develop functional kernel learning (FKL) to directly infer functional
posteriors over kernels. In particular, we place a transformed Gaussian process
over a spectral density, to induce a non-parametric distribution over kernel
functions. The resulting approach enables learning of rich representations,
with support for any stationary kernel, uncertainty over the values of the
kernel, and an interpretable specification of a prior directly over kernels,
without requiring sophisticated initialization or manual intervention. We
perform inference through elliptical slice sampling, which is especially well
suited to marginalizing posteriors with the strongly correlated priors typical
to function space modelling. We develop our approach for non-uniform,
large-scale, multi-task, and multidimensional data, and show promising
performance in a wide range of settings, including interpolation,
extrapolation, and kernel recovery experiments. | [
"cs.LG",
"stat.ML"
] |
We introduce a new pre-trainable generic representation for visual-linguistic
tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the
simple yet powerful Transformer model as the backbone, and extends it to take
both visual and linguistic embedded features as input. In it, each element of
the input is either of a word from the input sentence, or a region-of-interest
(RoI) from the input image. It is designed to fit for most of the
visual-linguistic downstream tasks. To better exploit the generic
representation, we pre-train VL-BERT on the massive-scale Conceptual Captions
dataset, together with text-only corpus. Extensive empirical analysis
demonstrates that the pre-training procedure can better align the
visual-linguistic clues and benefit the downstream tasks, such as visual
commonsense reasoning, visual question answering and referring expression
comprehension. It is worth noting that VL-BERT achieved the first place of
single model on the leaderboard of the VCR benchmark. Code is released at
\url{https://github.com/jackroos/VL-BERT}. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this paper, we introduce a new multi-task algorithm for a sequence
of reinforcement-learning tasks when each task is sampled independently from
(an unknown) distribution over a finite set of Markov decision processes whose
parameters are initially unknown. For this setting, we prove under certain
assumptions that the per-task sample complexity of exploration is reduced
significantly due to transfer compared to standard single-task algorithms. Our
multi-task algorithm also has the desired characteristic that it is guaranteed
not to exhibit negative transfer: in the worst case its per-task sample
complexity is comparable to the corresponding single-task algorithm. | [
"cs.LG",
"stat.ML"
] |
Physics-informed neural networks (PINNs) have lately received great attention
thanks to their flexibility in tackling a wide range of forward and inverse
problems involving partial differential equations. However, despite their
noticeable empirical success, little is known about how such constrained neural
networks behave during their training via gradient descent. More importantly,
even less is known about why such models sometimes fail to train at all. In
this work, we aim to investigate these questions through the lens of the Neural
Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected
neural networks in the infinite width limit during training via gradient
descent. Specifically, we derive the NTK of PINNs and prove that, under
appropriate conditions, it converges to a deterministic kernel that stays
constant during training in the infinite-width limit. This allows us to analyze
the training dynamics of PINNs through the lens of their limiting NTK and find
a remarkable discrepancy in the convergence rate of the different loss
components contributing to the total training error. To address this
fundamental pathology, we propose a novel gradient descent algorithm that
utilizes the eigenvalues of the NTK to adaptively calibrate the convergence
rate of the total training error. Finally, we perform a series of numerical
experiments to verify the correctness of our theory and the practical
effectiveness of the proposed algorithms. The data and code accompanying this
manuscript are publicly available at
\url{https://github.com/PredictiveIntelligenceLab/PINNsNTK}. | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] |
We present an approach to learn a dense pixel-wise labeling from image-level
tags. Each image-level tag imposes constraints on the output labeling of a
Convolutional Neural Network (CNN) classifier. We propose Constrained CNN
(CCNN), a method which uses a novel loss function to optimize for any set of
linear constraints on the output space (i.e. predicted label distribution) of a
CNN. Our loss formulation is easy to optimize and can be incorporated directly
into standard stochastic gradient descent optimization. The key idea is to
phrase the training objective as a biconvex optimization for linear models,
which we then relax to nonlinear deep networks. Extensive experiments
demonstrate the generality of our new learning framework. The constrained loss
yields state-of-the-art results on weakly supervised semantic image
segmentation. We further demonstrate that adding slightly more supervision can
greatly improve the performance of the learning algorithm. | [
"cs.CV",
"cs.LG"
] |
How to learn a discriminative fine-grained representation is a key point in
many computer vision applications, such as person re-identification,
fine-grained classification, fine-grained image retrieval, etc. Most of the
previous methods focus on learning metrics or ensemble to derive better global
representation, which are usually lack of local information. Based on the
considerations above, we propose a novel Attribute-Aware Attention Model
($A^3M$), which can learn local attribute representation and global category
representation simultaneously in an end-to-end manner. The proposed model
contains two attention models: attribute-guided attention module uses attribute
information to help select category features in different regions, at the same
time, category-guided attention module selects local features of different
attributes with the help of category cues. Through this attribute-category
reciprocal process, local and global features benefit from each other. Finally,
the resulting feature contains more intrinsic information for image recognition
instead of the noisy and irrelevant features. Extensive experiments conducted
on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the
effectiveness of our $A^3M$. Code is available at
https://github.com/iamhankai/attribute-aware-attention. | [
"cs.CV"
] |
In this paper, we propose a new learning technique named message-dropout to
improve the performance for multi-agent deep reinforcement learning under two
application scenarios: 1) classical multi-agent reinforcement learning with
direct message communication among agents and 2) centralized training with
decentralized execution. In the first application scenario of multi-agent
systems in which direct message communication among agents is allowed, the
message-dropout technique drops out the received messages from other agents in
a block-wise manner with a certain probability in the training phase and
compensates for this effect by multiplying the weights of the dropped-out block
units with a correction probability. The applied message-dropout technique
effectively handles the increased input dimension in multi-agent reinforcement
learning with communication and makes learning robust against communication
errors in the execution phase. In the second application scenario of
centralized training with decentralized execution, we particularly consider the
application of the proposed message-dropout to Multi-Agent Deep Deterministic
Policy Gradient (MADDPG), which uses a centralized critic to train a
decentralized actor for each agent. We evaluate the proposed message-dropout
technique for several games, and numerical results show that the proposed
message-dropout technique with proper dropout rate improves the reinforcement
learning performance significantly in terms of the training speed and the
steady-state performance in the execution phase. | [
"cs.LG",
"cs.AI",
"cs.MA"
] |
Standard reinforcement learning methods aim to master one way of solving a
task whereas there may exist multiple near-optimal policies. Being able to
identify this collection of near-optimal policies can allow a domain expert to
efficiently explore the space of reasonable solutions. Unfortunately, existing
approaches that quantify uncertainty over policies are not ultimately relevant
to finding policies with qualitatively distinct behaviors. In this work, we
formalize the difference between policies as a difference between the
distribution of trajectories induced by each policy, which encourages diversity
with respect to both state visitation and action choices. We derive a
gradient-based optimization technique that can be combined with existing policy
gradient methods to now identify diverse collections of well-performing
policies. We demonstrate our approach on benchmarks and a healthcare task. | [
"cs.LG",
"stat.ML"
] |
CNN feature spaces can be linearly mapped and consequently are often
interchangeable. This equivalence holds across variations in architectures,
training datasets, and network tasks. Specifically, we mapped between 10
image-classification CNNs and between 4 facial-recognition CNNs. When image
embeddings generated by one CNN are transformed into embeddings corresponding
to the feature space of a second CNN trained on the same task, their respective
image classification or face verification performance is largely preserved. For
CNNs trained to the same classes and sharing a common backend-logit (soft-max)
architecture, a linear-mapping may always be calculated directly from the
backend layer weights. However, the case of a closed-set analysis with perfect
knowledge of classifiers is limiting. Therefore, empirical methods of
estimating mappings are presented for both the closed-set image classification
task and the open-set task of face recognition. The results presented expose
the essentially interchangeable nature of CNNs embeddings for two important and
common recognition tasks. The implications are far-reaching, suggesting an
underlying commonality between representations learned by networks designed and
trained for a common task. One practical implication is that face embeddings
from some commonly used CNNs can be compared using these mappings. | [
"cs.CV",
"cs.LG"
] |
Low-light is an inescapable element of our daily surroundings that greatly
affects the efficiency of our vision. Research works on low-light has seen a
steady growth, particularly in the field of image enhancement, but there is
still a lack of a go-to database as benchmark. Besides, research fields that
may assist us in low-light environments, such as object detection, has glossed
over this aspect even though breakthroughs-after-breakthroughs had been
achieved in recent years, most noticeably from the lack of low-light data (less
than 2% of the total images) in successful public benchmark dataset such as
PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark
dataset to elevate this data drought, consisting exclusively of ten different
types of low-light images (i.e. low, ambient, object, single, weak, strong,
screen, window, shadow and twilight) captured in visible light only with image
and object level annotations. Moreover, we share insightful findings in regards
to the effects of low-light on the object detection task by analyzing
visualizations of both hand-crafted and learned features. Most importantly, we
found that the effects of low-light reaches far deeper into the features than
can be solved by simple "illumination invariance'". It is our hope that this
analysis and the Exclusively Dark dataset can encourage the growth in low-light
domain researches on different fields. The Exclusively Dark dataset with its
annotation is available at
https://github.com/cs-chan/Exclusively-Dark-Image-Dataset | [
"cs.CV"
] |
We make the first steps towards generalizing the theory of stochastic block
models, in the sparse regime, towards a model where the discrete community
structure is replaced by an underlying geometry. We consider a geometric random
graph over a homogeneous metric space where the probability of two vertices to
be connected is an arbitrary function of the distance. We give sufficient
conditions under which the locations can be recovered (up to an isomorphism of
the space) in the sparse regime. Moreover, we define a geometric counterpart of
the model of flow of information on trees, due to Mossel and Peres, in which
one considers a branching random walk on a sphere and the goal is to recover
the location of the root based on the locations of leaves. We give some
sufficient conditions for percolation and for non-percolation of information in
this model. | [
"stat.ML",
"cs.LG",
"cs.SI",
"math.PR"
] |
Multitask Reinforcement Learning is a promising way to obtain models with
better performance, generalisation, data efficiency, and robustness. Most
existing work is limited to compatible settings, where the state and action
space dimensions are the same across tasks. Graph Neural Networks (GNN) are one
way to address incompatible environments, because they can process graphs of
arbitrary size. They also allow practitioners to inject biases encoded in the
structure of the input graph. Existing work in graph-based continuous control
uses the physical morphology of the agent to construct the input graph, i.e.,
encoding limb features as node labels and using edges to connect the nodes if
their corresponded limbs are physically connected. In this work, we present a
series of ablations on existing methods that show that morphological
information encoded in the graph does not improve their performance. Motivated
by the hypothesis that any benefits GNNs extract from the graph structure are
outweighed by difficulties they create for message passing, we also propose
Amorpheus, a transformer-based approach. Further results show that, while
Amorpheus ignores the morphological information that GNNs encode, it
nonetheless substantially outperforms GNN-based methods that use the
morphological information to define the message-passing scheme. | [
"cs.LG",
"stat.ML"
] |
An efficient linear self-attention fusion model is proposed in this paper for
the task of hyperspectral image (HSI) and LiDAR data joint classification. The
proposed method is comprised of a feature extraction module, an attention
module, and a fusion module. The attention module is a plug-and-play linear
self-attention module that can be extensively used in any model. The proposed
model has achieved the overall accuracy of 95.40\% on the Houston dataset. The
experimental results demonstrate the superiority of the proposed method over
other state-of-the-art models. | [
"cs.CV",
"eess.IV"
] |
Image augmentation techniques apply transformation functions such as
rotation, shearing, or color distortion on an input image. These augmentations
were proven useful in improving neural networks' generalization ability. In
this paper, we present a novel augmentation operation, InAugment, that exploits
image internal statistics. The key idea is to copy patches from the image
itself, apply augmentation operations on them, and paste them back at random
positions on the same image. This method is simple and easy to implement and
can be incorporated with existing augmentation techniques. We test InAugment on
two popular datasets -- CIFAR and ImageNet. We show improvement over
state-of-the-art augmentation techniques. Incorporating InAugment with Auto
Augment yields a significant improvement over other augmentation techniques
(e.g., +1% improvement over multiple architectures trained on the CIFAR
dataset). We also demonstrate an increase for ResNet50 and EfficientNet-B3
top-1's accuracy on the ImageNet dataset compared to prior augmentation
methods. Finally, our experiments suggest that training convolutional neural
network using InAugment not only improves the model's accuracy and confidence
but its performance on out-of-distribution images. | [
"cs.CV"
] |
In practical situations, the ensemble tree model is one of the most popular
models along with neural networks. A soft tree is one of the variants of a
decision tree. Instead of using a greedy method for searching splitting rules,
the soft tree is trained using a gradient method in which the whole splitting
operation is formulated in a differentiable form. Although ensembles of such
soft trees have been increasingly used in recent years, little theoretical work
has been done for understanding their behavior. In this paper, by considering
an ensemble of infinite soft trees, we introduce and study the Tree Neural
Tangent Kernel (TNTK), which provides new insights into the behavior of the
infinite ensemble of soft trees. Using the TNTK, we succeed in theoretically
finding several non-trivial properties, such as the effect of the oblivious
tree structure and the degeneracy of the TNTK induced by the deepening of the
trees. Moreover, we empirically examine the performance of an ensemble of
infinite soft trees using the TNTK. | [
"cs.LG",
"stat.ML"
] |
Temporal grounding of natural language in untrimmed videos is a fundamental
yet challenging multimedia task facilitating cross-media visual content
retrieval. We focus on the weakly supervised setting of this task that merely
accesses to coarse video-level language description annotation without temporal
boundary, which is more consistent with reality as such weak labels are more
readily available in practice. In this paper, we propose a \emph{Boundary
Adaptive Refinement} (BAR) framework that resorts to reinforcement learning
(RL) to guide the process of progressively refining the temporal boundary. To
the best of our knowledge, we offer the first attempt to extend RL to temporal
localization task with weak supervision. As it is non-trivial to obtain a
straightforward reward function in the absence of pairwise granular
boundary-query annotations, a cross-modal alignment evaluator is crafted to
measure the alignment degree of segment-query pair to provide tailor-designed
rewards. This refinement scheme completely abandons traditional sliding window
based solution pattern and contributes to acquiring more efficient,
boundary-flexible and content-aware grounding results. Extensive experiments on
two public benchmarks Charades-STA and ActivityNet demonstrate that BAR
outperforms the state-of-the-art weakly-supervised method and even beats some
competitive fully-supervised ones. | [
"cs.CV"
] |
Graph neural networks (GNNs) have been widely used in deep learning on
graphs. They can learn effective node representations that achieve superior
performances in graph analysis tasks such as node classification and node
clustering. However, most methods ignore the heterogeneity in real-world
graphs. Methods designed for heterogeneous graphs, on the other hand, fail to
learn complex semantic representations because they only use meta-paths instead
of meta-graphs. Furthermore, they cannot fully capture the content-based
correlations between nodes, as they either do not use the self-attention
mechanism or only use it to consider the immediate neighbors of each node,
ignoring the higher-order neighbors. We propose a novel Higher-order
Attribute-Enhancing (HAE) framework that enhances node embedding in a
layer-by-layer manner. Under the HAE framework, we propose a Higher-order
Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network
representation learning. HAEGNN simultaneously incorporates meta-paths and
meta-graphs for rich, heterogeneous semantics, and leverages the self-attention
mechanism to explore content-based nodes interactions. The unique higher-order
architecture of HAEGNN allows examining the first-order as well as higher-order
neighborhoods. Moreover, HAEGNN shows good explainability as it learns the
importances of different meta-paths and meta-graphs. HAEGNN is also
memory-efficient, for it avoids per meta-path based matrix calculation.
Experimental results not only show HAEGNN superior performance against the
state-of-the-art methods in node classification, node clustering, and
visualization, but also demonstrate its superiorities in terms of memory
efficiency and explainability. | [
"cs.LG",
"cs.SI"
] |
One problem in the application of reinforcement learning to real-world
problems is the curse of dimensionality on the action space. Macro actions, a
sequence of primitive actions, have been studied to diminish the dimensionality
of the action space with regard to the time axis. However, previous studies
relied on humans defining macro actions or assumed macro actions as repetitions
of the same primitive actions. We present Factorized Macro Action Reinforcement
Learning (FaMARL) which autonomously learns disentangled factor representation
of a sequence of actions to generate macro actions that can be directly applied
to general reinforcement learning algorithms. FaMARL exhibits higher scores
than other reinforcement learning algorithms on environments that require an
extensive amount of search. | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.AP",
"stat.ML"
] |
Decision trees with binary splits are popularly constructed using
Classification and Regression Trees (CART) methodology. For binary
classification and regression models, this approach recursively divides the
data into two near-homogenous daughter nodes according to a split point that
maximizes the reduction in sum of squares error (the impurity) along a
particular variable. This paper aims to study the bias and adaptive properties
of regression trees constructed with CART. In doing so, we derive an
interesting connection between the bias and the mean decrease in impurity (MDI)
measure of variable importance---a tool widely used for model
interpretability---defined as the sum of impurity reductions over all
non-terminal nodes in the tree. In particular, we show that the probability
content of a terminal subnode for a variable is small when the MDI for that
variable is large and that this relationship is exponential---confirming
theoretically that decision trees with CART have small bias and are adaptive to
signal strength and direction. Finally, we apply these individual tree bounds
to tree ensembles and show consistency of Breiman's random forests. The context
is surprisingly general and applies to a wide variety of multivariable data
generating distributions and regression functions. The main technical tool is
an exact characterization of the conditional probability content of the
daughter nodes arising from an optimal split, in terms of the partial
dependence function and reduction in impurity. | [
"stat.ML",
"cs.LG"
] |
Monocular 3D object detection aims to extract the 3D position and properties
of objects from a 2D input image. This is an ill-posed problem with a major
difficulty lying in the information loss by depth-agnostic cameras.
Conventional approaches sample 3D bounding boxes from the space and infer the
relationship between the target object and each of them, however, the
probability of effective samples is relatively small in the 3D space. To
improve the efficiency of sampling, we propose to start with an initial
prediction and refine it gradually towards the ground truth, with only one 3d
parameter changed in each step. This requires designing a policy which gets a
reward after several steps, and thus we adopt reinforcement learning to
optimize it. The proposed framework, Reinforced Axial Refinement Network
(RAR-Net), serves as a post-processing stage which can be freely integrated
into existing monocular 3D detection methods, and improve the performance on
the KITTI dataset with small extra computational costs. | [
"cs.CV"
] |
In the past few years, various approaches have been developed to explain and
interpret deep neural network (DNN) representations, but it has been pointed
out that these representations are sometimes unstable and not reproducible. In
this paper, we interpret these representations as hypotheses driven by DNN
(called DNN-driven hypotheses) and propose a method to quantify the reliability
of these hypotheses in statistical hypothesis testing framework. To this end,
we introduce Selective Inference (SI) framework, which has received much
attention in the past few years as a new statistical inference framework for
data-driven hypotheses. The basic idea of SI is to make conditional inferences
on the selected hypotheses under the condition that they are selected. In order
to use SI framework for DNN representations, we develop a new SI algorithm
based on homotopy method which enables us to derive the exact (non-asymptotic)
conditional sampling distribution of the DNN-driven hypotheses. We conduct
experiments on both synthetic and real-world datasets, through which we offer
evidence that our proposed method can successfully control the false positive
rate, has decent performance in terms of computational efficiency, and provides
good results in practical applications. | [
"stat.ML",
"cs.CV",
"cs.LG"
] |
Deep neural networks (DNN) have shown remarkable success in the
classification of physiological signals. In this study we propose a method for
examining to what extent does a DNN's performance rely on rediscovering
existing features of the signals, as opposed to discovering genuinely new
features. Moreover, we offer a novel method of "removing" a hand-engineered
feature from the network's hypothesis space, thus forcing it to try and learn
representations which are different from known ones, as a method of scientific
exploration. We then build on existing work in the field of interpretability,
specifically class activation maps, to try and infer what new features the
network has learned. We demonstrate this approach using ECG and EEG signals.
With respect to ECG signals we show that for the specific task of classifying
atrial fibrillation, DNNs are likely rediscovering known features. We also show
how our method could be used to discover new features, by selectively removing
some ECG features and "rediscovering" them. We further examine how could our
method be used as a tool for examining scientific hypotheses. We simulate this
scenario by looking into the importance of eye movements in classifying sleep
from EEG. We show that our tool can successfully focus a researcher's attention
by bringing to light patterns in the data that would be hidden otherwise. | [
"stat.ML",
"cs.LG"
] |
Actor-critic methods, a type of model-free Reinforcement Learning, have been
successfully applied to challenging tasks in continuous control, often
achieving state-of-the art performance. However, wide-scale adoption of these
methods in real-world domains is made difficult by their poor sample
efficiency. We address this problem both theoretically and empirically. On the
theoretical side, we identify two phenomena preventing efficient exploration in
existing state-of-the-art algorithms such as Soft Actor Critic. First,
combining a greedy actor update with a pessimistic estimate of the critic leads
to the avoidance of actions that the agent does not know about, a phenomenon we
call pessimistic underexploration. Second, current algorithms are directionally
uninformed, sampling actions with equal probability in opposite directions from
the current mean. This is wasteful, since we typically need actions taken along
certain directions much more than others. To address both of these phenomena,
we introduce a new algorithm, Optimistic Actor Critic, which approximates a
lower and upper confidence bound on the state-action value function. This
allows us to apply the principle of optimism in the face of uncertainty to
perform directed exploration using the upper bound while still using the lower
bound to avoid overestimation. We evaluate OAC in several challenging
continuous control tasks, achieving state-of the art sample efficiency. | [
"stat.ML",
"cs.LG"
] |
Person re-identification (Re-ID) aims at recognizing the same person from
images taken across different cameras. To address this task, one typically
requires a large amount labeled data for training an effective Re-ID model,
which might not be practical for real-world applications. To alleviate this
limitation, we choose to exploit a sufficient amount of pre-existing labeled
data from a different (auxiliary) dataset. By jointly considering such an
auxiliary dataset and the dataset of interest (but without label information),
our proposed adaptation and re-identification network (ARN) performs
unsupervised domain adaptation, which leverages information across datasets and
derives domain-invariant features for Re-ID purposes. In our experiments, we
verify that our network performs favorably against state-of-the-art
unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID
methods which require fully supervised data for training. | [
"cs.CV"
] |
The application of Generative Pre-trained Transformer (GPT-2) to learn
text-archived game notation provides a model environment for exploring sparse
reward gameplay. The transformer architecture proves amenable to training on
solved text archives describing mazes, Rubik's Cube, and Sudoku solvers. The
method benefits from fine-tuning the transformer architecture to visualize
plausible strategies derived outside any guidance from human heuristics or
domain expertise. The large search space ($>10^{19}$) for the games provides a
puzzle environment in which the solution has few intermediate rewards and a
final move that solves the challenge. | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
Scene graph generation models understand the scene through object and
predicate recognition, but are prone to mistakes due to the challenges of
perception in the wild. Perception errors often lead to nonsensical
compositions in the output scene graph, which do not follow real-world rules
and patterns, and can be corrected using commonsense knowledge. We propose the
first method to acquire visual commonsense such as affordance and intuitive
physics automatically from data, and use that to improve the robustness of
scene understanding. To this end, we extend Transformer models to incorporate
the structure of scene graphs, and train our Global-Local Attention Transformer
on a scene graph corpus. Once trained, our model can be applied on any scene
graph generation model and correct its obvious mistakes, resulting in more
semantically plausible scene graphs. Through extensive experiments, we show our
model learns commonsense better than any alternative, and improves the accuracy
of state-of-the-art scene graph generation methods. | [
"cs.CV",
"cs.LG"
] |
Deep convolutional neural networks (DCNNs) trained for face identification
develop representations that generalize over variable images, while retaining
subject (e.g., gender) and image (e.g., viewpoint) information. Identity,
gender, and viewpoint codes were studied at the "neural unit" and ensemble
levels of a face-identification network. At the unit level, identification,
gender classification, and viewpoint estimation were measured by deleting units
to create variably-sized, randomly-sampled subspaces at the top network layer.
Identification of 3,531 identities remained high (area under the ROC
approximately 1.0) as dimensionality decreased from 512 units to 16 (0.95), 4
(0.80), and 2 (0.72) units. Individual identities separated statistically on
every top-layer unit. Cross-unit responses were minimally correlated,
indicating that units code non-redundant identity cues. This "distributed" code
requires only a sparse, random sample of units to identify faces accurately.
Gender classification declined gradually and viewpoint estimation fell steeply
as dimensionality decreased. Individual units were weakly predictive of gender
and viewpoint, but ensembles proved effective predictors. Therefore,
distributed and sparse codes co-exist in the network units to represent
different face attributes. At the ensemble level, principal component analysis
of face representations showed that identity, gender, and viewpoint information
separated into high-dimensional subspaces, ordered by explained variance.
Identity, gender, and viewpoint information contributed to all individual unit
responses, undercutting a neural tuning analogy for face attributes.
Interpretation of neural-like codes from DCNNs, and by analogy, high-level
visual codes, cannot be inferred from single unit responses. Instead, "meaning"
is encoded by directions in the high-dimensional space. | [
"cs.CV",
"cs.LG"
] |
Unsupervised image captioning with no annotations is an emerging challenge in
computer vision, where the existing arts usually adopt GAN (Generative
Adversarial Networks) models. In this paper, we propose a novel memory-based
network rather than GAN, named Recurrent Relational Memory Network ($R^2M$).
Unlike complicated and sensitive adversarial learning that non-ideally performs
for long sentence generation, $R^2M$ implements a concepts-to-sentence memory
translator through two-stage memory mechanisms: fusion and recurrent memories,
correlating the relational reasoning between common visual concepts and the
generated words for long periods. $R^2M$ encodes visual context through
unsupervised training on images, while enabling the memory to learn from
irrelevant textual corpus via supervised fashion. Our solution enjoys less
learnable parameters and higher computational efficiency than GAN-based
methods, which heavily bear parameter sensitivity. We experimentally validate
the superiority of $R^2M$ than state-of-the-arts on all benchmark datasets. | [
"cs.CV"
] |
In this paper, we present a deep learning model that exploits the power of
self-supervision to perform 3D point cloud completion, estimating the missing
part and a context region around it. Local and global information are encoded
in a combined embedding. A denoising pretext task provides the network with the
needed local cues, decoupled from the high-level semantics and naturally shared
over multiple classes. On the other hand, contrastive learning maximizes the
agreement between variants of the same shape with different missing portions,
thus producing a representation which captures the global appearance of the
shape. The combined embedding inherits category-agnostic properties from the
chosen pretext tasks. Differently from existing approaches, this allows to
better generalize the completion properties to new categories unseen at
training time. Moreover, while decoding the obtained joint representation, we
better blend the reconstructed missing part with the partial shape by paying
attention to its known surrounding region and reconstructing this frame as
auxiliary objective. Our extensive experiments and detailed ablation on the
ShapeNet dataset show the effectiveness of each part of the method with new
state of the art results. Our quantitative and qualitative analysis confirms
how our approach is able to work on novel categories without relying neither on
classification and shape symmetry priors, nor on adversarial training
procedures. | [
"cs.CV"
] |
In this paper, we propose a method to find local-geometry-aware traversal
directions on the intermediate latent space of Generative Adversarial Networks
(GANs). These directions are defined as an ordered basis of tangent space at a
latent code. Motivated by the intrinsic sparsity of the latent space, the basis
is discovered by solving the low-rank approximation problem of the differential
of the partial network. Moreover, the local traversal basis leads to a natural
iterative traversal on the latent space. Iterative Curve-Traversal shows stable
traversal on images, since the trajectory of latent code stays close to the
latent space even under the strong perturbations compared to the linear
traversal. This stability provides far more diverse variations of the given
image. Although the proposed method can be applied to various GAN models, we
focus on the W-space of the StyleGAN2, which is renowned for showing the better
disentanglement of the latent factors of variation. Our quantitative and
qualitative analysis provides evidence showing that the W-space is still
globally warped while showing a certain degree of global consistency of
interpretable variation. In particular, we introduce some metrics on the
Grassmannian manifolds to quantify the global warpage of the W-space and the
subspace traversal to test the stability of traversal directions. | [
"cs.CV"
] |
Deep neural models have hitherto achieved significant performances on
numerous classification tasks, but meanwhile require sufficient manually
annotated data. Since it is extremely time-consuming and expensive to annotate
adequate data for each classification task, learning an empirically effective
model with generalization on small dataset has received increased attention.
Existing efforts mainly focus on transferring task-relevant knowledge from
other similar data to tackle the issue. These approaches have yielded
remarkable improvements, yet neglecting the fact that the task-irrelevant
features could bring out massive negative transfer effects. To date, no
large-scale studies have been performed to investigate the impact of
task-irrelevant features, let alone the utilization of this kind of features.
In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to
exploit task-irrelevant features, which mainly are extracted from
task-irrelevant labels. Particularly, we suppress the expression of
task-irrelevant information and facilitate the learning process of
classification. We also provide a theoretical explanation of our method. In
addition, TIRTL does not conflict with those that have previously exploited
task-relevant knowledge and can be well combined to enable the simultaneous
utilization of task-relevant and task-irrelevant features for the first time.
In order to verify the effectiveness of our theory and method, we conduct
extensive experiments on facial expression recognition and digit recognition
tasks. Our source code will be also available in the future for
reproducibility. | [
"cs.LG"
] |
In recent years, increasingly augmentation of health data, such as patient
Electronic Health Records (EHR), are becoming readily available. This provides
an unprecedented opportunity for knowledge discovery and data mining algorithms
to dig insights from them, which can, later on, be helpful to the improvement
of the quality of care delivery. Predictive modeling of clinical risk,
including in-hospital mortality, hospital readmission, chronic disease onset,
condition exacerbation, etc., from patient EHR, is one of the health data
analytic problems that attract most of the interests. The reason is not only
because the problem is important in clinical settings, but also there are
challenges working with EHR such as sparsity, irregularity, temporality, etc.
Different from applications in other domains such as computer vision and
natural language processing, the labeled data samples in medicine (patients)
are relatively limited, which creates lots of troubles for effective predictive
model learning, especially for complicated models such as deep learning. In
this paper, we propose MetaPred, a meta-learning for clinical risk prediction
from longitudinal patient EHRs. In particular, in order to predict the target
risk where there are limited data samples, we train a meta-learner from a set
of related risk prediction tasks which learns how a good predictor is learned.
The meta-learned can then be directly used in target risk prediction, and the
limited available samples can be used for further fine-tuning the model
performance. The effectiveness of MetaPred is tested on a real patient EHR
repository from Oregon Health & Science University. We are able to demonstrate
that with CNN and RNN as base predictors, MetaPred can achieve much better
performance for predicting target risk with low resources comparing with the
predictor trained on the limited samples available for this risk. | [
"cs.LG",
"stat.ML"
] |
Although deep learning techniques have been successfully applied to many
tasks, interpreting deep neural network models is still a big challenge to us.
Recently, many works have been done on visualizing and analyzing the mechanism
of deep neural networks in the areas of image processing and natural language
processing. In this paper, we present our approaches to visualize and
understand deep neural networks for a very important commercial task--CTR
(Click-through rate) prediction. We conduct experiments on the productive data
from our online advertising system with daily varying distribution. To
understand the mechanism and the performance of the model, we inspect the
model's inner status at neuron level. Also, a probe approach is implemented to
measure the layer-wise performance of the model. Moreover, to measure the
influence from the input features, we calculate saliency scores based on the
back-propagated gradients. Practical applications are also discussed, for
example, in understanding, monitoring, diagnosing and refining models and
algorithms. | [
"stat.ML",
"cs.LG"
] |
Identifiability is a desirable property of a statistical model: it implies
that the true model parameters may be estimated to any desired precision, given
sufficient computational resources and data. We study identifiability in the
context of representation learning: discovering nonlinear data representations
that are optimal with respect to some downstream task. When parameterized as
deep neural networks, such representation functions typically lack
identifiability in parameter space, because they are overparameterized by
design. In this paper, building on recent advances in nonlinear ICA, we aim to
rehabilitate identifiability by showing that a large family of discriminative
models are in fact identifiable in function space, up to a linear
indeterminacy. Many models for representation learning in a wide variety of
domains have been identifiable in this sense, including text, images and audio,
state-of-the-art at time of publication. We derive sufficient conditions for
linear identifiability and provide empirical support for the result on both
simulated and real-world data. | [
"stat.ML",
"cs.LG"
] |
Most previous studies on multi-agent reinforcement learning focus on deriving
decentralized and cooperative policies to maximize a common reward and rarely
consider the transferability of trained policies to new tasks. This prevents
such policies from being applied to more complex multi-agent tasks. To resolve
these limitations, we propose a model that conducts both representation
learning for multiple agents using hierarchical graph attention network and
policy learning using multi-agent actor-critic. The hierarchical graph
attention network is specially designed to model the hierarchical relationships
among multiple agents that either cooperate or compete with each other to
derive more advanced strategic policies. Two attention networks, the
inter-agent and inter-group attention layers, are used to effectively model
individual and group level interactions, respectively. The two attention
networks have been proven to facilitate the transfer of learned policies to new
tasks with different agent compositions and allow one to interpret the learned
strategies. Empirically, we demonstrate that the proposed model outperforms
existing methods in several mixed cooperative and competitive tasks. | [
"cs.LG",
"cs.AI",
"cs.MA",
"stat.ML"
] |
Anderson acceleration is an old and simple method for accelerating the
computation of a fixed point. However, as far as we know and quite
surprisingly, it has never been applied to dynamic programming or reinforcement
learning. In this paper, we explain briefly what Anderson acceleration is and
how it can be applied to value iteration, this being supported by preliminary
experiments showing a significant speed up of convergence, that we critically
discuss. We also discuss how this idea could be applied more generally to
(deep) reinforcement learning. | [
"cs.LG",
"stat.ML"
] |
We propose a rejection sampling scheme using the discriminator of a GAN to
approximately correct errors in the GAN generator distribution. We show that
under quite strict assumptions, this will allow us to recover the data
distribution exactly. We then examine where those strict assumptions break down
and design a practical algorithm - called Discriminator Rejection Sampling
(DRS) - that can be used on real data-sets. Finally, we demonstrate the
efficacy of DRS on a mixture of Gaussians and on the SAGAN model,
state-of-the-art in the image generation task at the time of developing this
work. On ImageNet, we train an improved baseline that increases the Inception
Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65
to 14.79. We then use DRS to further improve on this baseline, improving the
Inception Score to 76.08 and the FID to 13.75. | [
"stat.ML",
"cs.LG"
] |
Effective training of advanced ML models requires large amounts of labeled
data, which is often scarce in scientific problems given the substantial human
labor and material cost to collect labeled data. This poses a challenge on
determining when and where we should deploy measuring instruments (e.g.,
in-situ sensors) to collect labeled data efficiently. This problem differs from
traditional pool-based active learning settings in that the labeling decisions
have to be made immediately after we observe the input data that come in a time
series. In this paper, we develop a real-time active learning method that uses
the spatial and temporal contextual information to select representative query
samples in a reinforcement learning framework. To reduce the need for large
training data, we further propose to transfer the policy learned from
simulation data which is generated by existing physics-based models. We
demonstrate the effectiveness of the proposed method by predicting streamflow
and water temperature in the Delaware River Basin given a limited budget for
collecting labeled data. We further study the spatial and temporal distribution
of selected samples to verify the ability of this method in selecting
informative samples over space and time. | [
"cs.LG",
"cs.AI"
] |
In this paper we propose the use of Generative Adversarial Networks (GAN) to
generate artificial training data for machine learning tasks. The generation of
artificial training data can be extremely useful in situations such as
imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also
useful when the data contains sensitive information, and it is desirable to
avoid using the original data set as much as possible (example: medical data).
We test our proposal on benchmark data sets using different network
architectures, and show that a Decision Tree (DT) classifier trained using the
training data generated by the GAN reached the same, (and surprisingly
sometimes better), accuracy and recall than a DT trained on the original data
set. | [
"cs.LG",
"stat.ML"
] |
This paper introduces a novel approach to in-painting where the identity of
the object to remove or change is preserved and accounted for at inference
time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize
exemplar information to produce high-quality, personalized in painting results.
We propose using exemplar information in the form of a reference image of the
region to in-paint, or a perceptual code describing that object. Unlike
previous conditional GAN formulations, this extra information can be inserted
at multiple points within the adversarial network, thus increasing its
descriptive power. We show that ExGANs can produce photo-realistic personalized
in-painting results that are both perceptually and semantically plausible by
applying them to the task of closed to-open eye in-painting in natural
pictures. A new benchmark dataset is also introduced for the task of eye
in-painting for future comparisons. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Multi-hop knowledge based question answering (KBQA) is a complex task for
natural language understanding. Many KBQA approaches have been proposed in
recent years, and most of them are trained based on labeled reasoning path.
This hinders the system's performance as many correct reasoning paths are not
labeled as ground truth, and thus they cannot be learned. In this paper, we
introduce an end-to-end KBQA system which can leverage multiple reasoning
paths' information and only requires labeled answer as supervision. We conduct
experiments on several benchmark datasets containing both single-hop simple
questions as well as muti-hop complex questions, including WebQuestionSP
(WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and
demonstrate strong performance. | [
"cs.LG",
"stat.ML"
] |
Object recognition is a fundamental problem in many video processing tasks,
accurately locating seen objects at low computation cost paves the way for
on-device video recognition. We propose PatchNet, an efficient convolutional
neural network to match objects in adjacent video frames. It learns the
patchwise correlation features instead of pixel features. PatchNet is very
compact, running at just 58MFLOPs, $5\times$ simpler than MobileNetV2. We
demonstrate its application on two tasks, video object detection and visual
object tracking. On ImageNet VID, PatchNet reduces the flops of R-FCN
ResNet-101 by 5x and EfficientDet-D0 by 3.4x with less than 1% mAP loss. On
OTB2015, PatchNet reduces SiamFC and SiamRPN by 2.5x with no accuracy loss.
Experiments on Jetson Nano further demonstrate 2.8x to 4.3x speed-ups
associated with flops reduction. Code is open sourced at
https://github.com/RalphMao/PatchNet. | [
"cs.CV",
"cs.AI"
] |
Although the expectation maximisation (EM) algorithm was introduced in 1970,
it remains somewhat inaccessible to machine learning practitioners due to its
obscure notation, terse proofs and lack of concrete links to modern machine
learning techniques like autoencoded variational Bayes. This has resulted in
gaps in the AI literature concerning the meaning of such concepts like "latent
variables" and "variational lower bound," which are frequently used but often
not clearly explained. The roots of these ideas lie in the EM algorithm. We
first give a tutorial presentation of the EM algorithm for estimating the
parameters of a $K$-component mixture density. The Gaussian mixture case is
presented in detail using $K$-ary scalar hidden (or latent) variables rather
than the more traditional binary valued $K$-dimenional vectors. This
presentation is motivated by mixture modelling from the target tracking
literature. In a similar style to Bishop's 2009 book, we present variational
Bayesian inference as a generalised EM algorithm stemming from the variational
(or evidential) lower bound, as well as the technique of mean field
approximation (or product density transform). We continue the evolution from EM
to variational autoencoders, developed by Kingma & Welling in 2014. In so
doing, we establish clear links between the EM algorithm and its variational
counterparts, hence clarifying the meaning of "latent variables." We provide a
detailed coverage of the "reparametrisation trick" and focus on how the AEVB
differs from conventional variational Bayesian inference. Throughout the
tutorial, consistent notational conventions are used. This unifies the
narrative and clarifies the concepts. Some numerical examples are given to
further illustrate the algorithms. | [
"stat.ML",
"cs.LG"
] |
We propose to incorporate adversarial dropout in generative multi-adversarial
networks, by omitting or dropping out, the feedback of each discriminator in
the framework with some probability at the end of each batch. Our approach
forces the single generator not to constrain its output to satisfy a single
discriminator, but, instead, to satisfy a dynamic ensemble of discriminators.
We show that this leads to a more generalized generator, promoting variety in
the generated samples and avoiding the common mode collapse problem commonly
experienced with generative adversarial networks (GANs). We further provide
evidence that the proposed framework, named Dropout-GAN, promotes sample
diversity both within and across epochs, eliminating mode collapse and
stabilizing training. | [
"cs.LG",
"stat.ML"
] |
We present a novel approach to perform the unsupervised domain adaptation for
object detection through forward-backward cyclic (FBC) training. Recent
adversarial training based domain adaptation methods have shown their
effectiveness on minimizing domain discrepancy via marginal feature
distributions alignment. However, aligning the marginal feature distributions
does not guarantee the alignment of class conditional distributions. This
limitation is more evident when adapting object detectors as the domain
discrepancy is larger compared to the image classification task, e.g. various
number of objects exist in one image and the majority of content in an image is
the background. This motivates us to learn domain invariance for category level
semantics via gradient alignment. Intuitively, if the gradients of two domains
point in similar directions, then the learning of one domain can improve that
of another domain. To achieve gradient alignment, we propose Forward-Backward
Cyclic Adaptation, which iteratively computes adaptation from source to target
via backward hopping and from target to source via forward passing. In
addition, we align low-level features for adapting holistic color/texture via
adversarial training. However, the detector performs well on both domains is
not ideal for target domain. As such, in each cycle, domain diversity is
enforced by maximum entropy regularization on the source domain to penalize
confident source-specific learning and minimum entropy regularization on target
domain to intrigue target-specific learning. Theoretical analysis of the
training process is provided, and extensive experiments on challenging
cross-domain object detection datasets have shown the superiority of our
approach over the state-of-the-art. | [
"cs.CV"
] |
Discovering causal structures from data is a challenging inference problem of
fundamental importance in all areas of science. The appealing scaling
properties of neural networks have recently led to a surge of interest in
differentiable neural network-based methods for learning causal structures from
data. So far differentiable causal discovery has focused on static datasets of
observational or interventional origin. In this work, we introduce an active
intervention-targeting mechanism which enables a quick identification of the
underlying causal structure of the data-generating process. Our method
significantly reduces the required number of interactions compared with random
intervention targeting and is applicable for both discrete and continuous
optimization formulations of learning the underlying directed acyclic graph
(DAG) from data. We examine the proposed method across a wide range of settings
and demonstrate superior performance on multiple benchmarks from simulated to
real-world data. | [
"stat.ML",
"cs.LG"
] |
The Ken Burns effect allows animating still images with a virtual camera scan
and zoom. Adding parallax, which results in the 3D Ken Burns effect, enables
significantly more compelling results. Creating such effects manually is
time-consuming and demands sophisticated editing skills. Existing automatic
methods, however, require multiple input images from varying viewpoints. In
this paper, we introduce a framework that synthesizes the 3D Ken Burns effect
from a single image, supporting both a fully automatic mode and an interactive
mode with the user controlling the camera. Our framework first leverages a
depth prediction pipeline, which estimates scene depth that is suitable for
view synthesis tasks. To address the limitations of existing depth estimation
methods such as geometric distortions, semantic distortions, and inaccurate
depth boundaries, we develop a semantic-aware neural network for depth
prediction, couple its estimate with a segmentation-based depth adjustment
process, and employ a refinement neural network that facilitates accurate depth
predictions at object boundaries. According to this depth estimate, our
framework then maps the input image to a point cloud and synthesizes the
resulting video frames by rendering the point cloud from the corresponding
camera positions. To address disocclusions while maintaining geometrically and
temporally coherent synthesis results, we utilize context-aware color- and
depth-inpainting to fill in the missing information in the extreme views of the
camera path, thus extending the scene geometry of the point cloud. Experiments
with a wide variety of image content show that our method enables realistic
synthesis results. Our study demonstrates that our system allows users to
achieve better results while requiring little effort compared to existing
solutions for the 3D Ken Burns effect creation. | [
"cs.CV",
"cs.GR"
] |
Voxelwise classification approaches are popular and effective methods for
tissue quantification in brain magnetic resonance imaging (MRI) scans. However,
generalization of these approaches is hampered by large differences between
sets of MRI scans such as differences in field strength, vendor or acquisition
protocols. Due to this acquisition related variation, classifiers trained on
data from a specific scanner fail or under-perform when applied to data that
was acquired differently. In order to address this lack of generalization, we
propose a Siamese neural network (MRAI-net) to learn a representation that
minimizes the between-scanner variation, while maintaining the contrast between
brain tissues necessary for brain tissue quantification. The proposed MRAI-net
was evaluated on both simulated and real MRI data. After learning the MR
acquisition invariant representation, any supervised classification model that
uses feature vectors can be applied. In this paper, we provide a proof of
principle, which shows that a linear classifier applied on the MRAI
representation is able to outperform supervised convolutional neural network
classifiers for tissue classification when little target training data is
available. | [
"cs.CV",
"stat.ML"
] |
As reinforcement learning techniques are increasingly applied to real-world
decision problems, attention has turned to how these algorithms use potentially
sensitive information. We consider the task of training a policy that maximizes
reward while minimizing disclosure of certain sensitive state variables through
the actions. We give examples of how this setting covers real-world problems in
privacy for sequential decision-making. We solve this problem in the policy
gradients framework by introducing a regularizer based on the mutual
information (MI) between the sensitive state and the actions at a given
timestep. We develop a model-based stochastic gradient estimator for
optimization of privacy-constrained policies. We also discuss an alternative MI
regularizer that serves as an upper bound to our main MI regularizer and can be
optimized in a model-free setting. We contrast previous work in
differentially-private RL to our mutual-information formulation of information
disclosure. Experimental results show that our training method results in
policies which hide the sensitive state. | [
"cs.LG",
"cs.CR"
] |
Several density estimation methods have shown to fail to detect
out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous
data. Energy-based models (EBMs) are flexible, unnormalized density models
which seem to be able to improve upon this failure mode. In this work, we
provide an extensive study investigating OOD detection with EBMs trained with
different approaches on tabular and image data and find that EBMs do not
provide consistent advantages. We hypothesize that EBMs do not learn semantic
features despite their discriminative structure similar to Normalizing Flows.
To verify this hypotheses, we show that supervision and architectural
restrictions improve the OOD detection of EBMs independent of the training
approach. | [
"cs.LG"
] |
Boundary discontinuity and its inconsistency to the final detection metric
have been the bottleneck for rotating detection regression loss design. In this
paper, we propose a novel regression loss based on Gaussian Wasserstein
distance as a fundamental approach to solve the problem. Specifically, the
rotated bounding box is converted to a 2-D Gaussian distribution, which enables
to approximate the indifferentiable rotational IoU induced loss by the Gaussian
Wasserstein distance (GWD) which can be learned efficiently by gradient
back-propagation. GWD can still be informative for learning even there is no
overlapping between two rotating bounding boxes which is often the case for
small object detection. Thanks to its three unique properties, GWD can also
elegantly solve the boundary discontinuity and square-like problem regardless
how the bounding box is defined. Experiments on five datasets using different
detectors show the effectiveness of our approach. Codes are available at
https://github.com/yangxue0827/RotationDetection. | [
"cs.CV",
"cs.AI"
] |
Language instruction plays an essential role in the natural language grounded
navigation tasks. However, navigators trained with limited human-annotated
instructions may have difficulties in accurately capturing key information from
the complicated instruction at different timesteps, leading to poor navigation
performance. In this paper, we exploit to train a more robust navigator which
is capable of dynamically extracting crucial factors from the long instruction,
by using an adversarial attacking paradigm. Specifically, we propose a Dynamic
Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the
navigator to move to the wrong target by destroying the most instructive
information in instructions at different timesteps. By formulating the
perturbation generation as a Markov Decision Process, DR-Attacker is optimized
by the reinforcement learning algorithm to generate perturbed instructions
sequentially during the navigation, according to a learnable attack score.
Then, the perturbed instructions, which serve as hard samples, are used for
improving the robustness of the navigator with an effective adversarial
training strategy and an auxiliary self-supervised reasoning task. Experimental
results on both Vision-and-Language Navigation (VLN) and Navigation from Dialog
History (NDH) tasks show the superiority of our proposed method over
state-of-the-art methods. Moreover, the visualization analysis shows the
effectiveness of the proposed DR-Attacker, which can successfully attack
crucial information in the instructions at different timesteps. Code is
available at https://github.com/expectorlin/DR-Attacker. | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
In real-world scenarios, it is appealing to learn a model carrying out
stochastic operations internally, known as stochastic computation graphs
(SCGs), rather than learning a deterministic mapping. However, standard
backpropagation is not applicable to SCGs. We attempt to address this issue
from the angle of cost propagation, with local surrogate costs, called
Q-functions, constructed and learned for each stochastic node in an SCG. Then,
the SCG can be trained based on these surrogate costs using standard
backpropagation. We propose the entire framework as a solution to generalize
backpropagation for SCGs, which resembles an actor-critic architecture but
based on a graph. For broad applicability, we study a variety of SCG structures
from one cost to multiple costs. We utilize recent advances in reinforcement
learning (RL) and variational Bayes (VB), such as off-policy critic learning
and unbiased-and-low-variance gradient estimation, and review them in the
context of SCGs. The generalized backpropagation extends transported learning
signals beyond gradients between stochastic nodes while preserving the benefit
of backpropagating gradients through deterministic nodes. Experimental
suggestions and concerns are listed to help design and test any specific model
using this framework. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
3D object detector based on Hough voting achieves great success and derives
many follow-up works. Despite constantly refreshing the detection accuracy,
these works suffer from handcrafted components used to eliminate redundant
boxes, and thus are non-end-to-end and time-consuming. In this work, we propose
a suppress-and-refine framework to remove these handcrafted components. To
fully utilize full-resolution information and achieve real-time speed, it
directly consumes feature points and redundant 3D proposals. Specifically, it
first suppresses noisy 3D feature points and then feeds them to 3D proposals
for the following RoI-aware refinement. With the gating mechanism to build fine
proposal features and the self-attention mechanism to model relationships, our
method can produce high-quality predictions with a small computation budget in
an end-to-end manner. To this end, we present the first fully end-to-end 3D
detector, SRDet, on the basis of VoteNet. It achieves state-of-the-art
performance on the challenging ScanNetV2 and SUN RGB-D datasets with the
fastest speed ever. Our code will be available at
https://github.com/ZJULearning/SRDet. | [
"cs.CV"
] |
Design of new drug compounds with target properties is a key area of research
in generative modeling. We present a small drug molecule design pipeline based
on graph-generative models and a comparison study of two state-of-the-art graph
generative models for designing COVID-19 targeted drug candidates: 1) a
variational autoencoder-based approach (VAE) that uses prior knowledge of
molecules that have been shown to be effective for earlier coronavirus
treatments and 2) a deep Q-learning method (DQN) that generates optimized
molecules without any proximity constraints. We evaluate the novelty of the
automated molecule generation approaches by validating the candidate molecules
with drug-protein binding affinity models. The VAE method produced two novel
molecules with similar structures to the antiretroviral protease inhibitor
Indinavir that show potential binding affinity for the SARS-CoV-2 protein
target 3-chymotrypsin-like protease (3CL-protease). | [
"cs.LG",
"cs.AI"
] |
Learning about many things can provide numerous benefits to a reinforcement
learning system. For example, learning many auxiliary value functions, in
addition to optimizing the environmental reward, appears to improve both
exploration and representation learning. The question we tackle in this paper
is how to sculpt the stream of experience---how to adapt the learning system's
behavior---to optimize the learning of a collection of value functions. A
simple answer is to compute an intrinsic reward based on the statistics of each
auxiliary learner, and use reinforcement learning to maximize that intrinsic
reward. Unfortunately, implementing this simple idea has proven difficult, and
thus has been the focus of decades of study. It remains unclear which of the
many possible measures of learning would work well in a parallel learning
setting where environmental reward is extremely sparse or absent. In this
paper, we investigate and compare different intrinsic reward mechanisms in a
new bandit-like parallel-learning testbed. We discuss the interaction between
reward and prediction learners and highlight the importance of introspective
prediction learners: those that increase their rate of learning when progress
is possible, and decrease when it is not. We provide a comprehensive empirical
comparison of 14 different rewards, including well-known ideas from
reinforcement learning and active learning. Our results highlight a simple but
seemingly powerful principle: intrinsic rewards based on the amount of learning
can generate useful behavior, if each individual learner is introspective. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
The prototypical network (ProtoNet) is a few-shot learning framework that
performs metric learning and classification using the distance to prototype
representations of each class. It has attracted a great deal of attention
recently since it is simple to implement, highly extensible, and performs well
in experiments. However, it only takes into account the mean of the support
vectors as prototypes and thus it performs poorly when the support set has high
variance. In this paper, we propose to combine ProtoNet with local Fisher
discriminant analysis to reduce the local within-class covariance and increase
the local between-class covariance of the support set. We show the usefulness
of the proposed method by theoretically providing an expected risk bound and
empirically demonstrating its superior classification accuracy on miniImageNet
and tieredImageNet. | [
"cs.LG",
"stat.ML",
"68T01(Primary), 68T05(Secondary)"
] |
It is crucial to reduce natural gas methane emissions, which can potentially
offset the climate benefits of replacing coal with gas. Optical gas imaging
(OGI) is a widely-used method to detect methane leaks, but is labor-intensive
and cannot provide leak detection results without operators' judgment. In this
paper, we develop a computer vision approach to OGI-based leak detection using
convolutional neural networks (CNN) trained on methane leak images to enable
automatic detection. First, we collect ~1 M frames of labeled video of methane
leaks from different leaking equipment for building CNN model, covering a wide
range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m).
Second, we examine different background subtraction methods to extract the
methane plume in the foreground. Third, we then test three CNN model variants,
collectively called GasNet, to detect plumes in videos taken at other pieces of
leaking equipment. We assess the ability of GasNet to perform leak detection by
comparing it to a baseline method that uses optical-flow based change detection
algorithm. We explore the sensitivity of results to the CNN structure, with a
moderate-complexity variant performing best across distances. We find that the
detection accuracy can reach as high as 99%, the overall detection accuracy can
exceed 95% for a case across all leak sizes and imaging distances. Binary
detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely
(~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater
than 94% accuracy across all leak sizes. At farthest distances (~13-16 m),
performance degrades rapidly, but it can achieve above 95% accuracy to detect
large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be
deployed in OGI surveys to allow automatic vigilance of methane leak detection
with high detection accuracy in the real world. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
We consider the problem of learning by demonstration from agents acting in
unknown stochastic Markov environments or games. Our aim is to estimate agent
preferences in order to construct improved policies for the same task that the
agents are trying to solve. To do so, we extend previous probabilistic
approaches for inverse reinforcement learning in known MDPs to the case of
unknown dynamics or opponents. We do this by deriving two simplified
probabilistic models of the demonstrator's policy and utility. For
tractability, we use maximum a posteriori estimation rather than full Bayesian
inference. Under a flat prior, this results in a convex optimisation problem.
We find that the resulting algorithms are highly competitive against a variety
of other methods for inverse reinforcement learning that do have knowledge of
the dynamics. | [
"stat.ML",
"cs.LG"
] |
Exploration is essential for reinforcement learning (RL). To face the
challenges of exploration, we consider a reward-free RL framework that
completely separates exploration from exploitation and brings new challenges
for exploration algorithms. In the exploration phase, the agent learns an
exploratory policy by interacting with a reward-free environment and collects a
dataset of transitions by executing the policy. In the planning phase, the
agent computes a good policy for any reward function based on the dataset
without further interacting with the environment. This framework is suitable
for the meta RL setting where there are many reward functions of interest. In
the exploration phase, we propose to maximize the Renyi entropy over the
state-action space and justify this objective theoretically. The success of
using Renyi entropy as the objective results from its encouragement to explore
the hard-to-reach state-actions. We further deduce a policy gradient
formulation for this objective and design a practical exploration algorithm
that can deal with complex environments. In the planning phase, we solve for
good policies given arbitrary reward functions using a batch RL algorithm.
Empirically, we show that our exploration algorithm is effective and sample
efficient, and results in superior policies for arbitrary reward functions in
the planning phase. | [
"cs.LG",
"stat.ML"
] |
Recently, Generative Adversarial Network (GAN) has been found wide
applications in style transfer, image-to-image translation and image
super-resolution. In this paper, a color-depth conditional GAN is proposed to
concurrently resolve the problems of depth super-resolution and color
super-resolution in 3D videos. Firstly, given the low-resolution depth image
and low-resolution color image, a generative network is proposed to leverage
mutual information of color image and depth image to enhance each other in
consideration of the geometry structural dependency of color-depth image in the
same scene. Secondly, three loss functions, including data loss, total
variation loss, and 8-connected gradient difference loss are introduced to
train this generative network in order to keep generated images close to the
real ones, in addition to the adversarial loss. Experimental results
demonstrate that the proposed approach produces high-quality color image and
depth image from low-quality image pair, and it is superior to several other
leading methods. Besides, we use the same neural network framework to resolve
the problem of image smoothing and edge detection at the same time. | [
"cs.CV"
] |
This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained by means of continuous Conditional Random Fields
(CRFs). In particular, we propose two different variations, one based on a
cascade of multiple CRFs, the other on a unified graphical model. By designing
a novel CNN implementation of mean-field updates for continuous CRFs, we show
that both proposed models can be regarded as sequential deep networks and that
training can be performed end-to-end. Through extensive experimental evaluation
we demonstrate the effective- ness of the proposed approach and establish new
state of the art results on publicly available datasets. | [
"cs.CV"
] |
Video classification is highly important with wide applications, such as
video search and intelligent surveillance. Video naturally consists of static
and motion information, which can be represented by frame and optical flow.
Recently, researchers generally adopt the deep networks to capture the static
and motion information \textbf{\emph{separately}}, which mainly has two
limitations: (1) Ignoring the coexistence relationship between spatial and
temporal attention, while they should be jointly modelled as the spatial and
temporal evolutions of video, thus discriminative video features can be
extracted.(2) Ignoring the strong complementarity between static and motion
information coexisted in video, while they should be collaboratively learned to
boost each other. For addressing the above two limitations, this paper proposes
the approach of two-stream collaborative learning with spatial-temporal
attention (TCLSTA), which consists of two models: (1) Spatial-temporal
attention model: The spatial-level attention emphasizes the salient regions in
frame, and the temporal-level attention exploits the discriminative frames in
video. They are jointly learned and mutually boosted to learn the
discriminative static and motion features for better classification
performance. (2) Static-motion collaborative model: It not only achieves mutual
guidance on static and motion information to boost the feature learning, but
also adaptively learns the fusion weights of static and motion streams, so as
to exploit the strong complementarity between static and motion information to
promote video classification. Experiments on 4 widely-used datasets show that
our TCLSTA approach achieves the best performance compared with more than 10
state-of-the-art methods. | [
"cs.CV"
] |
Vehicle Re-ID has recently attracted enthusiastic attention due to its
potential applications in smart city and urban surveillance. However, it
suffers from large intra-class variation caused by view variations and
illumination changes, and inter-class similarity especially for different
identities with the similar appearance. To handle these issues, in this paper,
we propose a novel deep network architecture, which guided by meaningful
attributes including camera views, vehicle types and colors for vehicle Re-ID.
In particular, our network is end-to-end trained and contains three subnetworks
of deep features embedded by the corresponding attributes (i.e., camera view,
vehicle type and vehicle color). Moreover, to overcome the shortcomings of
limited vehicle images of different views, we design a view-specified
generative adversarial network to generate the multi-view vehicle images. For
network training, we annotate the view labels on the VeRi-776 dataset. Note
that one can directly adopt the pre-trained view (as well as type and color)
subnetwork on the other datasets with only ID information, which demonstrates
the generalization of our model. Extensive experiments on the benchmark
datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the
promising performance and yields to a new state-of-the-art for vehicle Re-ID. | [
"cs.CV"
] |
We present a novel approach to generating photo-realistic images of a face
with accurate lip sync, given an audio input. By using a recurrent neural
network, we achieved mouth landmarks based on audio features. We exploited the
power of conditional generative adversarial networks to produce
highly-realistic face conditioned on a set of landmarks. These two networks
together are capable of producing a sequence of natural faces in sync with an
input audio track. | [
"cs.CV"
] |
Proximal policy optimization (PPO) algorithm is a deep reinforcement learning
algorithm with outstanding performance, especially in continuous control tasks.
But the performance of this method is still affected by its exploration
ability. For classical reinforcement learning, there are some schemes that make
exploration more full and balanced with data exploitation, but they can't be
applied in complex environments due to the complexity of algorithm. Based on
continuous control tasks with dense reward, this paper analyzes the assumption
of the original Gaussian action exploration mechanism in PPO algorithm, and
clarifies the influence of exploration ability on performance. Afterward,
aiming at the problem of exploration, an exploration enhancement mechanism
based on uncertainty estimation is designed in this paper. Then, we apply
exploration enhancement theory to PPO algorithm and propose the proximal policy
optimization algorithm with intrinsic exploration module (IEM-PPO) which can be
used in complex environments. In the experimental parts, we evaluate our method
on multiple tasks of MuJoCo physical simulator, and compare IEM-PPO algorithm
with curiosity driven exploration algorithm (ICM-PPO) and original algorithm
(PPO). The experimental results demonstrate that IEM-PPO algorithm needs longer
training time, but performs better in terms of sample efficiency and cumulative
reward, and has stability and robustness. | [
"cs.LG"
] |
We propose policy-gradient algorithms for solving the problem of control in a
risk-sensitive reinforcement learning (RL) context. The objective of our
algorithm is to maximize the distorted risk measure (DRM) of the cumulative
reward in an episodic Markov decision process (MDP). We derive a variant of the
policy gradient theorem that caters to the DRM objective. Using this theorem in
conjunction with a likelihood ratio (LR) based gradient estimation scheme, we
propose policy gradient algorithms for optimizing DRM in both on-policy and
off-policy RL settings. We derive non-asymptotic bounds that establish the
convergence of our algorithms to an approximate stationary point of the DRM
objective. | [
"cs.LG"
] |
Super-resolution fluorescence microscopy, with a resolution beyond the
diffraction limit of light, has become an indispensable tool to directly
visualize biological structures in living cells at a nanometer-scale
resolution. Despite advances in high-density super-resolution fluorescent
techniques, existing methods still have bottlenecks, including extremely long
execution time, artificial thinning and thickening of structures, and lack of
ability to capture latent structures. Here we propose a novel deep learning
guided Bayesian inference approach, DLBI, for the time-series analysis of
high-density fluorescent images. Our method combines the strength of deep
learning and statistical inference, where deep learning captures the underlying
distribution of the fluorophores that are consistent with the observed
time-series fluorescent images by exploring local features and correlation
along time-axis, and statistical inference further refines the ultrastructure
extracted by deep learning and endues physical meaning to the final image.
Comprehensive experimental results on both real and simulated datasets
demonstrate that our method provides more accurate and realistic local patch
and large-field reconstruction than the state-of-the-art method, the 3B
analysis, while our method is more than two orders of magnitude faster. The
main program is available at https://github.com/lykaust15/DLBI | [
"cs.CV",
"stat.AP",
"stat.ML"
] |
With the goal of recovering high-quality image content from its degraded
version, image restoration enjoys numerous applications, such as in
surveillance, computational photography, medical imaging, and remote sensing.
Recently, convolutional neural networks (CNNs) have achieved dramatic
improvements over conventional approaches for image restoration task. Existing
CNN-based methods typically operate either on full-resolution or on
progressively low-resolution representations. In the former case, spatially
precise but contextually less robust results are achieved, while in the latter
case, semantically reliable but spatially less accurate outputs are generated.
In this paper, we present a novel architecture with the collective goals of
maintaining spatially-precise high-resolution representations through the
entire network and receiving strong contextual information from the
low-resolution representations. The core of our approach is a multi-scale
residual block containing several key elements: (a) parallel multi-resolution
convolution streams for extracting multi-scale features, (b) information
exchange across the multi-resolution streams, (c) spatial and channel attention
mechanisms for capturing contextual information, and (d) attention based
multi-scale feature aggregation. In a nutshell, our approach learns an enriched
set of features that combines contextual information from multiple scales,
while simultaneously preserving the high-resolution spatial details. Extensive
experiments on five real image benchmark datasets demonstrate that our method,
named as MIRNet, achieves state-of-the-art results for a variety of image
processing tasks, including image denoising, super-resolution, and image
enhancement. The source code and pre-trained models are available at
https://github.com/swz30/MIRNet. | [
"cs.CV"
] |
Some tasks, such as surface normals or single-view depth estimation, require
per-pixel ground truth that is difficult to obtain on real images but easy to
obtain on synthetic. However, models learned on synthetic images often do not
generalize well to real images due to the domain shift. Our key idea to improve
domain adaptation is to introduce a separate anchor task (such as facial
landmarks) whose annotations can be obtained at no cost or are already
available on both synthetic and real datasets. To further leverage the implicit
relationship between the anchor and main tasks, we apply our \freeze technique
that learns the cross-task guidance on the source domain with the final network
layers, and use it on the target domain. We evaluate our methods on surface
normal estimation on two pairs of datasets (indoor scenes and faces) with two
kinds of anchor tasks (semantic segmentation and facial landmarks). We show
that blindly applying domain adaptation or training the auxiliary task on only
one domain may hurt performance, while using anchor tasks on both domains is
better behaved. Our \freeze technique outperforms competing approaches,
reaching performance in facial images on par with a recently popular surface
normal estimation method using shape from shading domain knowledge. | [
"cs.CV",
"stat.ML"
] |
We consider the problem of imitation learning from expert demonstrations in
partially observable Markov decision processes (POMDPs). Belief
representations, which characterize the distribution over the latent states in
a POMDP, have been modeled using recurrent neural networks and probabilistic
latent variable models, and shown to be effective for reinforcement learning in
POMDPs. In this work, we investigate the belief representation learning problem
for generative adversarial imitation learning in POMDPs. Instead of training
the belief module and the policy separately as suggested in prior work, we
learn the belief module jointly with the policy, using a task-aware imitation
loss to ensure that the representation is more aligned with the policy's
objective. To improve robustness of representation, we introduce several
informative belief regularization techniques, including multi-step prediction
of dynamics and action-sequences. Evaluated on various partially observable
continuous-control locomotion tasks, our belief-module imitation learning
approach (BMIL) substantially outperforms several baselines, including the
original GAIL algorithm and the task-agnostic belief learning algorithm.
Extensive ablation analysis indicates the effectiveness of task-aware belief
learning and belief regularization. | [
"cs.LG",
"stat.ML"
] |
Traditionally, in supervised machine learning, (a significant) part of the
available data (usually 50% to 80%) is used for training and the rest for
validation. In many problems, however, the data is highly imbalanced in regard
to different classes or does not have good coverage of the feasible data space
which, in turn, creates problems in validation and usage phase. In this paper,
we propose a technique for synthesising feasible and likely data to help
balance the classes as well as to boost the performance in terms of confusion
matrix as well as overall. The idea, in a nutshell, is to synthesise data
samples in close vicinity to the actual data samples specifically for the less
represented (minority) classes. This has also implications to the so-called
fairness of machine learning. In this paper, we propose a specific method for
synthesising data in a way to balance the classes and boost the performance,
especially of the minority classes. It is generic and can be applied to
different base algorithms, e.g. support vector machine, k-nearest neighbour,
deep networks, rule-based classifiers, decision trees, etc. The results
demonstrated that: i) a significantly more balanced (and fair) classification
results can be achieved; ii) that the overall performance as well as the
performance per class measured by confusion matrix can be boosted. In addition,
this approach can be very valuable for the cases when the number of actual
available labelled data is small which itself is one of the problems of the
contemporary machine learning. | [
"cs.LG",
"stat.ML"
] |
The method of random projection (RP) is the standard technique in machine
learning and many other areas, for dimensionality reduction, approximate near
neighbor search, compressed sensing, etc. Basically, RP provides a simple and
effective scheme for approximating pairwise inner products and Euclidean
distances in massive data. Closely related to RP, the method of random Fourier
features (RFF) has also become popular, for approximating the Gaussian kernel.
RFF applies a specific nonlinear transformation on the projected data from
random projections. In practice, using the (nonlinear) Gaussian kernel often
leads to better performance than the linear kernel (inner product), partly due
to the tuning parameter $(\gamma)$ introduced in the Gaussian kernel. Recently,
there has been a surge of interest in studying properties of RFF.
After random projections, quantization is an important step for efficient
data storage, computation, and transmission. Quantization for RP has also been
extensive studied in the literature. In this paper, we focus on developing
quantization algorithms for RFF. The task is in a sense challenging due to the
tuning parameter $\gamma$ in the Gaussian kernel. For example, the quantizer
and the quantized data might be tied to each specific tuning parameter
$\gamma$. Our contribution begins with an interesting discovery, that the
marginal distribution of RFF is actually free of the Gaussian kernel parameter
$\gamma$. This small finding significantly simplifies the design of the
Lloyd-Max (LM) quantization scheme for RFF in that there would be only one LM
quantizer for RFF (regardless of $\gamma$). We also develop a variant named
LM$^2$-RFF quantizer, which in certain cases is more accurate. Experiments
confirm that the proposed quantization schemes perform well. | [
"stat.ML",
"cs.LG"
] |
Aiming to produce sufficient and diverse training samples, data augmentation
has been demonstrated for its effectiveness in training deep models. Regarding
that the criterion of the best augmentation is challenging to define, we in
this paper present a novel learning-based augmentation method termed as
DeepAugNet, which formulates the final augmented data as a collection of
several sequentially augmented subsets. Specifically, the current augmented
subset is required to maximize the performance improvement compared with the
last augmented subset by learning the deterministic augmentation policy using
deep reinforcement learning. By introducing an unified optimization goal,
DeepAugNet intends to combine the data augmentation and the deep model training
in an end-to-end training manner which is realized by simultaneously training a
hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep
model. We extensively evaluated our proposed DeepAugNet on various benchmark
datasets including Fashion MNIST, CUB, CIFAR-100 and WebCaricature. Compared
with the current state-of-the-arts, our method can achieve a significant
improvement in small-scale datasets, and a comparable performance in
large-scale datasets. Code will be available soon. | [
"cs.CV",
"cs.LG"
] |
Recent work has increased the performance of Generative Adversarial Networks
(GANs) by enforcing a consistency cost on the discriminator. We improve on this
technique in several ways. We first show that consistency regularization can
introduce artifacts into the GAN samples and explain how to fix this issue. We
then propose several modifications to the consistency regularization procedure
designed to improve its performance. We carry out extensive experiments
quantifying the benefit of our improvements. For unconditional image synthesis
on CIFAR-10 and CelebA, our modifications yield the best known FID scores on
various GAN architectures. For conditional image synthesis on CIFAR-10, we
improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on
ImageNet-2012, we apply our technique to the original BigGAN model and improve
the FID from 6.66 to 5.38, which is the best score at that model size. | [
"stat.ML",
"cs.LG"
] |
Resolving the exploration-exploitation trade-off remains a fundamental
problem in the design and implementation of reinforcement learning (RL)
algorithms. In this paper, we focus on model-free RL using the epsilon-greedy
exploration policy, which despite its simplicity, remains one of the most
frequently used forms of exploration. However, a key limitation of this policy
is the specification of $\varepsilon$. In this paper, we provide a novel
Bayesian perspective of $\varepsilon$ as a measure of the uniformity of the
Q-value function. We introduce a closed-form Bayesian model update based on
Bayesian model combination (BMC), based on this new perspective, which allows
us to adapt $\varepsilon$ using experiences from the environment in constant
time with monotone convergence guarantees. We demonstrate that our proposed
algorithm, $\varepsilon$-\texttt{BMC}, efficiently balances exploration and
exploitation on different problems, performing comparably or outperforming the
best tuned fixed annealing schedules and an alternative data-dependent
$\varepsilon$ adaptation scheme proposed in the literature. | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
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