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Machine learning lies at the heart of new possibilities for scientific
discovery, knowledge generation, and artificial intelligence. Its potential
benefits to these fields requires going beyond predictive accuracy and focusing
on interpretability. In particular, many scientific problems require
interpretations in a domain-specific interpretable feature space (e.g. the
frequency domain) whereas attributions to the raw features (e.g. the pixel
space) may be unintelligible or even misleading. To address this challenge, we
propose TRIM (TRansformation IMportance), a novel approach which attributes
importances to features in a transformed space and can be applied post-hoc to a
fully trained model. TRIM is motivated by a cosmological parameter estimation
problem using deep neural networks (DNNs) on simulated data, but it is
generally applicable across domains/models and can be combined with any local
interpretation method. In our cosmology example, combining TRIM with contextual
decomposition shows promising results for identifying which frequencies a DNN
uses, helping cosmologists to understand and validate that the model learns
appropriate physical features rather than simulation artifacts. | [
"stat.ML",
"astro-ph.IM",
"cs.LG"
] |
We describe a new approach for managing aleatoric uncertainty in the
Reinforcement Learning (RL) paradigm. Instead of selecting actions according to
a single statistic, we propose a distributional method based on the
second-order stochastic dominance (SSD) relation. This compares the inherent
dispersion of random returns induced by actions, producing a more comprehensive
and robust evaluation of the environment's uncertainty. The necessary
conditions for SSD require estimators to predict accurate second moments. To
accommodate this, we map the distributional RL problem to a Wasserstein
gradient flow, treating the distributional Bellman residual as a potential
energy functional. We propose a particle-based algorithm for which we prove
optimality and convergence. Our experiments characterize the algorithm
performance and demonstrate how uncertainty and performance are better balanced
using an \textsc{ssd} policy than with other risk measures. | [
"cs.LG",
"stat.ML"
] |
We demonstrate an improved vision system that learns a model of its
environment using a self-supervised, predictive learning method. The system
includes a pan-tilt camera, a foveated visual input, a saccading reflex to
servo the foveated region to areas high prediction error, input frame
transformation synced to the camera motion, and a recursive, hierachical
machine learning technique based on the Predictive Vision Model. In earlier
work, which did not integrate camera motion into the vision model, prediction
was impaired and camera movement suffered from undesired feedback effects. Here
we detail the integration of camera motion into the predictive learning system
and show improved visual prediction and saccadic behavior. From these
experiences, we speculate on the integration of additional sensory and motor
systems into self-supervised, predictive learning models. | [
"cs.CV"
] |
Generative adversarial networks (GANs) provide a way to learn deep
representations without extensively annotated training data. They achieve this
through deriving backpropagation signals through a competitive process
involving a pair of networks. The representations that can be learned by GANs
may be used in a variety of applications, including image synthesis, semantic
image editing, style transfer, image super-resolution and classification. The
aim of this review paper is to provide an overview of GANs for the signal
processing community, drawing on familiar analogies and concepts where
possible. In addition to identifying different methods for training and
constructing GANs, we also point to remaining challenges in their theory and
application. | [
"cs.CV"
] |
We propose a scalable Bayesian preference learning method for jointly
predicting the preferences of individuals as well as the consensus of a crowd
from pairwise labels. Peoples' opinions often differ greatly, making it
difficult to predict their preferences from small amounts of personal data.
Individual biases also make it harder to infer the consensus of a crowd when
there are few labels per item. We address these challenges by combining matrix
factorisation with Gaussian processes, using a Bayesian approach to account for
uncertainty arising from noisy and sparse data. Our method exploits input
features, such as text embeddings and user metadata, to predict preferences for
new items and users that are not in the training set. As previous solutions
based on Gaussian processes do not scale to large numbers of users, items or
pairwise labels, we propose a stochastic variational inference approach that
limits computational and memory costs. Our experiments on a recommendation task
show that our method is competitive with previous approaches despite our
scalable inference approximation. We demonstrate the method's scalability on a
natural language processing task with thousands of users and items, and show
improvements over the state of the art on this task. We make our software
publicly available for future work. | [
"cs.LG",
"cs.CL",
"cs.HC",
"stat.ML"
] |
Previous versions of sparse principal component analysis (PCA) have presumed
that the eigen-basis (a $p \times k$ matrix) is approximately sparse. We
propose a method that presumes the $p \times k$ matrix becomes approximately
sparse after a $k \times k$ rotation. The simplest version of the algorithm
initializes with the leading $k$ principal components. Then, the principal
components are rotated with an $k \times k$ orthogonal rotation to make them
approximately sparse. Finally, soft-thresholding is applied to the rotated
principal components. This approach differs from prior approaches because it
uses an orthogonal rotation to approximate a sparse basis. One consequence is
that a sparse component need not to be a leading eigenvector, but rather a
mixture of them. In this way, we propose a new (rotated) basis for sparse PCA.
In addition, our approach avoids "deflation" and multiple tuning parameters
required for that. Our sparse PCA framework is versatile; for example, it
extends naturally to a two-way analysis of a data matrix for simultaneous
dimensionality reduction of rows and columns. We provide evidence showing that
for the same level of sparsity, the proposed sparse PCA method is more stable
and can explain more variance compared to alternative methods. Through three
applications -- sparse coding of images, analysis of transcriptome sequencing
data, and large-scale clustering of social networks, we demonstrate the modern
usefulness of sparse PCA in exploring multivariate data. | [
"stat.ML",
"cs.CV",
"cs.LG",
"stat.CO",
"stat.ME"
] |
Building neural network classifiers with an ability to distinguish between in
and out-of distribution inputs is an important step towards faithful deep
learning systems. Some of the successful approaches for this, resort to
architectural novelties, such as ensembles, with increased complexities in
terms of the number of parameters and training procedures. Whereas some other
approaches make use of surrogate samples, which are easy to create and work as
proxies for actual out-of-distribution (OOD) samples, to train the networks for
OOD detection. In this paper, we propose a very simple approach for enhancing
the ability of a pretrained network to detect OOD inputs without even altering
the original parameter values. We define a probabilistic trust interval for
each weight parameter of the network and optimize its size according to the
in-distribution (ID) inputs. It allows the network to sample additional weight
values along with the original values at the time of inference and use the
observed disagreement among the corresponding outputs for OOD detection. In
order to capture the disagreement effectively, we also propose a measure and
establish its suitability using empirical evidence. Our approach outperforms
the existing state-of-the-art methods on various OOD datasets by considerable
margins without using any real or surrogate OOD samples. We also analyze the
performance of our approach on adversarial and corrupted inputs such as
CIFAR-10-C and demonstrate its ability to clearly distinguish such inputs as
well. By using fundamental theorem of calculus on neural networks, we explain
why our technique doesn't need to observe OOD samples during training to
achieve results better than the previous works. | [
"cs.LG",
"cs.AI"
] |
Designing a logo is a long, complicated, and expensive process for any
designer. However, recent advancements in generative algorithms provide models
that could offer a possible solution. Logos are multi-modal, have very few
categorical properties, and do not have a continuous latent space. Yet,
conditional generative adversarial networks can be used to generate logos that
could help designers in their creative process. We propose LoGAN: an improved
auxiliary classifier Wasserstein generative adversarial neural network (with
gradient penalty) that is able to generate logos conditioned on twelve
different colors. In 768 generated instances (12 classes and 64 logos per
class), when looking at the most prominent color, the conditional generation
part of the model has an overall precision and recall of 0.8 and 0.7
respectively. LoGAN's results offer a first glance at how artificial
intelligence can be used to assist designers in their creative process and open
promising future directions, such as including more descriptive labels which
will provide a more exhaustive and easy-to-use system. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Face recognition technology has demonstrated tremendous progress over the
past few years, primarily due to advances in representation learning. As we
witness the widespread adoption of these systems, it is imperative to consider
the security of face representations. In this paper, we explore the
practicality of using a fully homomorphic encryption based framework to secure
a database of face templates. This framework is designed to preserve the
privacy of users and prevent information leakage from the templates, while
maintaining their utility through template matching directly in the encrypted
domain. Additionally, we also explore a batching and dimensionality reduction
scheme to trade-off face matching accuracy and computational complexity.
Experiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that
secure face matching can be practically feasible (16 KB template size and 0.01
sec per match pair for 512-dimensional features from SphereFace) while
exhibiting minimal loss in matching performance. | [
"cs.CV"
] |
The problem of finding the sparsest vector (direction) in a low dimensional
subspace can be considered as a homogeneous variant of the sparse recovery
problem, which finds applications in robust subspace recovery, dictionary
learning, sparse blind deconvolution, and many other problems in signal
processing and machine learning. However, in contrast to the classical sparse
recovery problem, the most natural formulation for finding the sparsest vector
in a subspace is usually nonconvex. In this paper, we overview recent advances
on global nonconvex optimization theory for solving this problem, ranging from
geometric analysis of its optimization landscapes, to efficient optimization
algorithms for solving the associated nonconvex optimization problem, to
applications in machine intelligence, representation learning, and imaging
sciences. Finally, we conclude this review by pointing out several interesting
open problems for future research. | [
"cs.LG",
"cs.IT",
"eess.IV",
"math.IT",
"math.OC",
"stat.ML"
] |
We introduce in this work the normalizing field flows (NFF) for learning
random fields from scattered measurements. More precisely, we construct a
bijective transformation (a normalizing flow characterizing by neural networks)
between a Gaussian random field with the Karhunen-Lo\`eve (KL) expansion
structure and the target stochastic field, where the KL expansion coefficients
and the invertible networks are trained by maximizing the sum of the
log-likelihood on scattered measurements. This NFF model can be used to solve
data-driven forward, inverse, and mixed forward/inverse stochastic partial
differential equations in a unified framework. We demonstrate the capability of
the proposed NFF model for learning Non Gaussian processes and different types
of stochastic partial differential equations. | [
"cs.LG",
"cs.NA",
"math.NA"
] |
Despite Generative Adversarial Networks (GANs) have been widely used in
various image-to-image translation tasks, they can be hardly applied on mobile
devices due to their heavy computation and storage cost. Traditional network
compression methods focus on visually recognition tasks, but never deal with
generation tasks. Inspired by knowledge distillation, a student generator of
fewer parameters is trained by inheriting the low-level and high-level
information from the original heavy teacher generator. To promote the
capability of student generator, we include a student discriminator to measure
the distances between real images, and images generated by student and teacher
generators. An adversarial learning process is therefore established to
optimize student generator and student discriminator. Qualitative and
quantitative analysis by conducting experiments on benchmark datasets
demonstrate that the proposed method can learn portable generative models with
strong performance. | [
"cs.CV",
"cs.LG",
"eess.IV",
"stat.ML"
] |
Conventional transfer learning leverages weights of pre-trained networks, but
mandates the need for similar neural architectures. Alternatively, knowledge
distillation can transfer knowledge between heterogeneous networks but often
requires access to the original training data or additional generative
networks. Knowledge transfer between networks can be improved by being agnostic
to the choice of network architecture and reducing the dependence on original
training data. We propose a knowledge transfer approach from a teacher to a
student network wherein we train the student on an independent transferal
dataset, whose annotations are generated by the teacher. Experiments were
conducted on five state-of-the-art networks for semantic segmentation and seven
datasets across three imaging modalities. We studied knowledge transfer from a
single teacher, combination of knowledge transfer and fine-tuning, and
knowledge transfer from multiple teachers. The student model with a single
teacher achieved similar performance as the teacher; and the student model with
multiple teachers achieved better performance than the teachers. The salient
features of our algorithm include: 1)no need for original training data or
generative networks, 2) knowledge transfer between different architectures, 3)
ease of implementation for downstream tasks by using the downstream task
dataset as the transferal dataset, 4) knowledge transfer of an ensemble of
models, trained independently, into one student model. Extensive experiments
demonstrate that the proposed algorithm is effective for knowledge transfer and
easily tunable. | [
"cs.CV"
] |
To reduce passenger waiting time and driver search friction, ride-hailing
companies need to accurately forecast spatio-temporal demand and supply-demand
gap. However, due to spatio-temporal dependencies pertaining to demand and
supply-demand gap in a ride-hailing system, making accurate forecasts for both
demand and supply-demand gap is a difficult task. Furthermore, due to
confidentiality and privacy issues, ride-hailing data are sometimes released to
the researchers by removing spatial adjacency information of the zones, which
hinders the detection of spatio-temporal dependencies. To that end, a novel
spatio-temporal deep learning architecture is proposed in this paper for
forecasting demand and supply-demand gap in a ride-hailing system with
anonymized spatial adjacency information, which integrates feature importance
layer with a spatio-temporal deep learning architecture containing
one-dimensional convolutional neural network (CNN) and zone-distributed
independently recurrent neural network (IndRNN). The developed architecture is
tested with real-world datasets of Didi Chuxing, which shows that our models
based on the proposed architecture can outperform conventional time-series
models (e.g., ARIMA) and machine learning models (e.g., gradient boosting
machine, distributed random forest, generalized linear model, artificial neural
network). Additionally, the feature importance layer provides an interpretation
of the model by revealing the contribution of the input features utilized in
prediction. | [
"cs.LG"
] |
RGB-thermal salient object detection (SOD) aims to segment the common
prominent regions of visible image and corresponding thermal infrared image
that we call it RGBT SOD. Existing methods don't fully explore and exploit the
potentials of complementarity of different modalities and multi-type cues of
image contents, which play a vital role in achieving accurate results. In this
paper, we propose a multi-interactive dual-decoder to mine and model the
multi-type interactions for accurate RGBT SOD. In specific, we first encode two
modalities into multi-level multi-modal feature representations. Then, we
design a novel dual-decoder to conduct the interactions of multi-level
features, two modalities and global contexts. With these interactions, our
method works well in diversely challenging scenarios even in the presence of
invalid modality. Finally, we carry out extensive experiments on public RGBT
and RGBD SOD datasets, and the results show that the proposed method achieves
the outstanding performance against state-of-the-art algorithms. The source
code has been released
at:https://github.com/lz118/Multi-interactive-Dual-decoder. | [
"cs.CV"
] |
Point cloud segmentation is a fundamental task in 3D. Despite recent progress
on point cloud segmentation with the power of deep networks, current deep
learning methods based on the clean label assumptions may fail with noisy
labels. Yet, object class labels are often mislabeled in real-world point cloud
datasets. In this work, we take the lead in solving this issue by proposing a
novel Point Noise-Adaptive Learning (PNAL) framework. Compared to existing
noise-robust methods on image tasks, our PNAL is noise-rate blind, to cope with
the spatially variant noise rate problem specific to point clouds.
Specifically, we propose a novel point-wise confidence selection to obtain
reliable labels based on the historical predictions of each point. A novel
cluster-wise label correction is proposed with a voting strategy to generate
the best possible label taking the neighbor point correlations into
consideration. We conduct extensive experiments to demonstrate the
effectiveness of PNAL on both synthetic and real-world noisy datasets. In
particular, even with $60\%$ symmetric noisy labels, our proposed method
produces much better results than its baseline counterpart without PNAL and is
comparable to the ideal upper bound trained on a completely clean dataset.
Moreover, we fully re-labeled the validation set of a popular but noisy
real-world scene dataset ScanNetV2 to make it clean, for rigorous experiment
and future research. Our code and data are available at
\url{https://shuquanye.com/PNAL_website/}. | [
"cs.CV",
"cs.GR"
] |
Agents trained by reinforcement learning (RL) often fail to generalize beyond
the environment they were trained in, even when presented with new scenarios
that seem similar to the training environment. We study the query complexity
required to train RL agents that generalize to multiple environments.
Intuitively, tractable generalization is only possible when the environments
are similar or close in some sense. To capture this, we introduce Weak
Proximity, a natural structural condition that requires the environments to
have highly similar transition and reward functions and share a policy
providing optimal value. Despite such shared structure, we prove that tractable
generalization is impossible in the worst case. This holds even when each
individual environment can be efficiently solved to obtain an optimal linear
policy, and when the agent possesses a generative model. Our lower bound
applies to the more complex task of representation learning for the purpose of
efficient generalization to multiple environments. On the positive side, we
introduce Strong Proximity, a strengthened condition which we prove is
sufficient for efficient generalization. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Current VHR(Very High Resolution) satellite images enable the detailed
monitoring of the earth and can capture the ongoing works of railway
construction. In this paper, we present an integrated framework applied to
monitoring the railway construction in China, using QuickBird, GF-2 and Google
Earth VHR satellite images. We also construct a novel DCNNs-based (Deep
Convolutional Neural Networks) semantic segmentation network to label the
temporary works such as borrow & spoil area, camp, beam yard and
ESAs(Environmental Sensitive Areas) such as resident houses throughout the
whole railway construction project using VHR satellite images. In addition, we
employ HED edge detection sub-network to refine the boundary details and
attention cross entropy loss function to fit the sample class disequilibrium
problem. Our semantic segmentation network is trained on 572 VHR true color
images, and tested on the 15 QuickBird true color images along
Ruichang-Jiujiang railway during 2015-2017. The experiment results show that
compared with the existing state-of-the-art approach, our approach has obvious
improvements with an overall accuracy of more than 80%. | [
"cs.CV"
] |
Accurate traffic prediction is crucial to the guidance and management of
urban traffics. However, most of the existing traffic prediction models do not
consider the computational burden and memory space when they capture
spatial-temporal dependence among traffic data. In this work, we propose a
factorized Spatial-Temporal Tensor Graph Convolutional Network to deal with
traffic speed prediction. Traffic networks are modeled and unified into a graph
that integrates spatial and temporal information simultaneously. We further
extend graph convolution into tensor space and propose a tensor graph
convolution network to extract more discriminating features from
spatial-temporal graph data. To reduce the computational burden, we take Tucker
tensor decomposition and derive factorized a tensor convolution, which performs
separate filtering in small-scale space, time, and feature modes. Besides, we
can benefit from noise suppression of traffic data when discarding those
trivial components in the process of tensor decomposition. Extensive
experiments on two real-world traffic speed datasets demonstrate our method is
more effective than those traditional traffic prediction methods, and meantime
achieves state-of-the-art performance. | [
"cs.LG"
] |
Deep learning technique has dramatically boosted the performance of face
alignment algorithms. However, due to large variability and lack of samples,
the alignment problem in unconstrained situations, \emph{e.g}\onedot large head
poses, exaggerated expression, and uneven illumination, is still largely
unsolved. In this paper, we explore the instincts and reasons behind our two
proposals, \emph{i.e}\onedot Propagation Module and Focal Wing Loss, to tackle
the problem. Concretely, we present a novel structure-infused face alignment
algorithm based on heatmap regression via propagating landmark heatmaps to
boundary heatmaps, which provide structure information for further attention
map generation. Moreover, we propose a Focal Wing Loss for mining and
emphasizing the difficult samples under in-the-wild condition. In addition, we
adopt methods like CoordConv and Anti-aliased CNN from other fields that
address the shift-variance problem of CNN for face alignment. When implementing
extensive experiments on different benchmarks, \emph{i.e}\onedot WFLW, 300W,
and COFW, our method outperforms state-of-the-arts by a significant margin. Our
proposed approach achieves 4.05\% mean error on WFLW, 2.93\% mean error on 300W
full-set, and 3.71\% mean error on COFW. | [
"cs.CV"
] |
Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with
that of other vehicles to enhance perceptive ability, and thus improve
detection accuracy and driving safety. However, multi-vehicle cooperative
perception requires the integration of real world scenes and the traffic of raw
sensor data exchange far exceeds the bandwidth of existing vehicular networks.
To the best our knowledge, we are the first to conduct a study on raw-data
level cooperative perception for enhancing the detection ability of
self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse
the sensor data collected from different positions and angles of connected
vehicles. A point cloud based 3D object detection method is proposed to work on
a diversity of aligned point clouds. Experimental results on KITTI and our
collected dataset show that the proposed system outperforms perception by
extending sensing area, improving detection accuracy and promoting augmented
results. Most importantly, we demonstrate it is possible to transmit point
clouds data for cooperative perception via existing vehicular network
technologies. | [
"cs.CV"
] |
In the deep metric learning approach to image segmentation, a convolutional
net densely generates feature vectors at the pixels of an image. Pairs of
feature vectors are trained to be similar or different, depending on whether
the corresponding pixels belong to same or different ground truth segments. To
segment a new image, the feature vectors are computed and clustered. Both
empirically and theoretically, it is unclear whether or when deep metric
learning is superior to the more conventional approach of directly predicting
an affinity graph with a convolutional net. We compare the two approaches using
brain images from serial section electron microscopy images, which constitute
an especially challenging example of instance segmentation. We first show that
seed-based postprocessing of the feature vectors, as originally proposed,
produces inferior accuracy because it is difficult for the convolutional net to
predict feature vectors that remain uniform across large objects. Then we
consider postprocessing by thresholding a nearest neighbor graph followed by
connected components. In this case, segmentations from a "metric graph" turn
out to be competitive or even superior to segmentations from a directly
predicted affinity graph. To explain these findings theoretically, we invoke
the property that the metric function satisfies the triangle inequality. Then
we show with an example where this constraint suppresses noise, causing
connected components to more robustly segment a metric graph than an
unconstrained affinity graph. | [
"cs.CV"
] |
To improve driving safety and avoid car accidents, Advanced Driver Assistance
Systems (ADAS) are given significant attention. Recent studies have focused on
predicting driver intention as a key part of these systems. In this study, we
proposed new framework in which 4 inputs are employed to anticipate diver
maneuver using Brain4Cars dataset and the maneuver prediction is achieved from
5, 4, 3, 2, 1 seconds before the actual action occurs. We evaluated our
framework in three scenarios: using only 1) inside view 2) outside view and 3)
both inside and outside view. We divided the dataset into training, validation
and test sets, also K-fold cross validation is utilized. Compared with
state-of-the-art studies, our architecture is faster and achieved higher
performance in second and third scenario. Accuracy, precision, recall and
f1-score as evaluation metrics were utilized and the result of 82.41%, 82.28%,
82,42% and 82.24% for outside view and 98.90%, 98.96%, 98.90% and 98.88% for
both inside and outside view were gained, respectively. | [
"cs.CV"
] |
Deep learning methods have achieved promising performance in many areas, but
they are still struggling with noisy-labeled images during the training
process. Considering that the annotation quality indispensably relies on great
expertise, the problem is even more crucial in the medical image domain. How to
eliminate the disturbance from noisy labels for segmentation tasks without
further annotations is still a significant challenge. In this paper, we
introduce our label quality evaluation strategy for deep neural networks
automatically assessing the quality of each label, which is not explicitly
provided, and training on clean-annotated ones. We propose a solution for
network automatically evaluating the relative quality of the labels in the
training set and using good ones to tune the network parameters. We also design
an overfitting control module to let the network maximally learn from the
precise annotations during the training process. Experiments on the public
biomedical image segmentation dataset have proved the method outperforms
baseline methods and retains both high accuracy and good generalization at
different noise levels. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Controllable semantic image editing enables a user to change entire image
attributes with a few clicks, e.g., gradually making a summer scene look like
it was taken in winter. Classic approaches for this task use a Generative
Adversarial Net (GAN) to learn a latent space and suitable latent-space
transformations. However, current approaches often suffer from attribute edits
that are entangled, global image identity changes, and diminished
photo-realism. To address these concerns, we learn multiple attribute
transformations simultaneously, integrate attribute regression into the
training of transformation functions, and apply a content loss and an
adversarial loss that encourages the maintenance of image identity and
photo-realism. We propose quantitative evaluation strategies for measuring
controllable editing performance, unlike prior work, which primarily focuses on
qualitative evaluation. Our model permits better control for both single- and
multiple-attribute editing while preserving image identity and realism during
transformation. We provide empirical results for both natural and synthetic
images, highlighting that our model achieves state-of-the-art performance for
targeted image manipulation. | [
"cs.CV"
] |
The heterogeneous network is a robust data abstraction that can model
entities of different types interacting in various ways. Such heterogeneity
brings rich semantic information but presents nontrivial challenges in
aggregating the heterogeneous relationships between objects - especially those
of higher-order indirect relations. Recent graph neural network approaches for
representation learning on heterogeneous networks typically employ the
attention mechanism, which is often only optimized for predictions based on
direct links. Furthermore, even though most deep learning methods can aggregate
higher-order information by building deeper models, such a scheme can diminish
the degree of interpretability. To overcome these challenges, we explore an
architecture - Layer-stacked ATTention Embedding (LATTE) - that automatically
decomposes higher-order meta relations at each layer to extract the relevant
heterogeneous neighborhood structures for each node. Additionally, by
successively stacking layer representations, the learned node embedding offers
a more interpretable aggregation scheme for nodes of different types at
different neighborhood ranges. We conducted experiments on several benchmark
heterogeneous network datasets. In both transductive and inductive node
classification tasks, LATTE can achieve state-of-the-art performance compared
to existing approaches, all while offering a lightweight model. With extensive
experimental analyses and visualizations, the framework can demonstrate the
ability to extract informative insights on heterogeneous networks. | [
"cs.LG",
"stat.ML"
] |
Despite the recent successes of reinforcement learning in games and robotics,
it is yet to become broadly practical. Sample efficiency and unreliable
performance in rare but challenging scenarios are two of the major obstacles.
Drawing inspiration from the effectiveness of deliberate practice for achieving
expert-level human performance, we propose a new adversarial sampling approach
guided by a failure predictor named "CoachNet". CoachNet is trained online
along with the agent to predict the probability of failure. This probability is
then used in a stochastic sampling process to guide the agent to more
challenging episodes. This way, instead of wasting time on scenarios that the
agent has already mastered, training is focused on the agent's "weak spots". We
present the design of CoachNet, explain its underlying principles, and
empirically demonstrate its effectiveness in improving sample efficiency and
test-time robustness in common continuous control tasks. | [
"cs.LG",
"cs.RO"
] |
One of the most popular approaches to understanding feature effects of modern
black box machine learning models are partial dependence plots (PDP). These
plots are easy to understand but only able to visualize low order dependencies.
The paper is about the question 'How much can we see?': A framework is
developed to quantify the explainability of arbitrary machine learning models,
i.e. up to what degree the visualization as given by a PDP is able to explain
the predictions of the model. The result allows for a judgement whether an
attempt to explain a black box model is sufficient or not. | [
"stat.ML",
"cs.LG"
] |
Image segmentation is the process of partitioning the image into significant
regions easier to analyze. Nowadays, segmentation has become a necessity in
many practical medical imaging methods as locating tumors and diseases. Hidden
Markov Random Field model is one of several techniques used in image
segmentation. It provides an elegant way to model the segmentation process.
This modeling leads to the minimization of an objective function. Conjugate
Gradient algorithm (CG) is one of the best known optimization techniques. This
paper proposes the use of the Conjugate Gradient algorithm (CG) for image
segmentation, based on the Hidden Markov Random Field. Since derivatives are
not available for this expression, finite differences are used in the CG
algorithm to approximate the first derivative. The approach is evaluated using
a number of publicly available images, where ground truth is known. The Dice
Coefficient is used as an objective criterion to measure the quality of
segmentation. The results show that the proposed CG approach compares favorably
with other variants of Hidden Markov Random Field segmentation algorithms. | [
"cs.CV"
] |
Network pruning has been the driving force for the acceleration of neural
networks and the alleviation of model storage/transmission burden. With the
advent of AutoML and neural architecture search (NAS), pruning has become
topical with automatic mechanism and searching based architecture optimization.
Yet, current automatic designs rely on either reinforcement learning or
evolutionary algorithm. Due to the non-differentiability of those algorithms,
the pruning algorithm needs a long searching stage before reaching the
convergence.
To circumvent this problem, this paper introduces a differentiable pruning
method via hypernetworks for automatic network pruning. The specifically
designed hypernetworks take latent vectors as input and generate the weight
parameters of the backbone network. The latent vectors control the output
channels of the convolutional layers in the backbone network and act as a
handle for the pruning of the layers. By enforcing $\ell_1$ sparsity
regularization to the latent vectors and utilizing proximal gradient solver,
sparse latent vectors can be obtained. Passing the sparsified latent vectors
through the hypernetworks, the corresponding slices of the generated weight
parameters can be removed, achieving the effect of network pruning. The latent
vectors of all the layers are pruned together, resulting in an automatic layer
configuration. Extensive experiments are conducted on various networks for
image classification, single image super-resolution, and denoising. And the
experimental results validate the proposed method. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Molecular graph generation is a fundamental problem for drug discovery and
has been attracting growing attention. The problem is challenging since it
requires not only generating chemically valid molecular structures but also
optimizing their chemical properties in the meantime. Inspired by the recent
progress in deep generative models, in this paper we propose a flow-based
autoregressive model for graph generation called GraphAF. GraphAF combines the
advantages of both autoregressive and flow-based approaches and enjoys: (1)
high model flexibility for data density estimation; (2) efficient parallel
computation for training; (3) an iterative sampling process, which allows
leveraging chemical domain knowledge for valency checking. Experimental results
show that GraphAF is able to generate 68% chemically valid molecules even
without chemical knowledge rules and 100% valid molecules with chemical rules.
The training process of GraphAF is two times faster than the existing
state-of-the-art approach GCPN. After fine-tuning the model for goal-directed
property optimization with reinforcement learning, GraphAF achieves
state-of-the-art performance on both chemical property optimization and
constrained property optimization. | [
"cs.LG",
"stat.ML"
] |
Learning maps between data samples is fundamental. Applications range from
representation learning, image translation and generative modeling, to the
estimation of spatial deformations. Such maps relate feature vectors, or map
between feature spaces. Well-behaved maps should be regular, which can be
imposed explicitly or may emanate from the data itself. We explore what induces
regularity for spatial transformations, e.g., when computing image
registrations. Classical optimization-based models compute maps between pairs
of samples and rely on an appropriate regularizer for well-posedness. Recent
deep learning approaches have attempted to avoid using such regularizers
altogether by relying on the sample population instead. We explore if it is
possible to obtain spatial regularity using an inverse consistency loss only
and elucidate what explains map regularity in such a context. We find that deep
networks combined with an inverse consistency loss and randomized off-grid
interpolation yield well behaved, approximately diffeomorphic, spatial
transformations. Despite the simplicity of this approach, our experiments
present compelling evidence, on both synthetic and real data, that regular maps
can be obtained without carefully tuned explicit regularizers, while achieving
competitive registration performance. | [
"cs.CV"
] |
Rain streaks degrade the image quality and seriously affect the performance
of subsequent computer vision tasks, such as autonomous driving, social
security, etc. Therefore, removing rain streaks from a given rainy images is of
great significance. Convolutional neural networks(CNN) have been widely used in
image deraining tasks, however, the local computational characteristics of
convolutional operations limit the development of image deraining tasks.
Recently, the popular transformer has global computational features that can
further facilitate the development of image deraining tasks. In this paper, we
introduce Swin-transformer into the field of image deraining for the first time
to study the performance and potential of Swin-transformer in the field of
image deraining. Specifically, we improve the basic module of Swin-transformer
and design a three-branch model to implement single-image rain removal. The
former implements the basic rain pattern feature extraction, while the latter
fuses different features to further extract and process the image features. In
addition, we employ a jump connection to fuse deep features and shallow
features. In terms of experiments, the existing public dataset suffers from
image duplication and relatively homogeneous background. So we propose a new
dataset Rain3000 to validate our model. Therefore, we propose a new dataset
Rain3000 for validating our model. Experimental results on the publicly
available datasets Rain100L, Rain100H and our dataset Rain3000 show that our
proposed method has performance and inference speed advantages over the current
mainstream single-image rain streaks removal models.The source code will be
available at https://github.com/H-tfx/SDNet. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Using insight from numerical approximation of ODEs and the problem
formulation and solution methodology of TD learning through a Galerkin
relaxation, I propose a new class of TD learning algorithms. After applying the
improved numerical methods, the parameter being approximated has a guaranteed
order of magnitude reduction in the Taylor Series error of the solution to the
ODE for the parameter $\theta(t)$ that is used in constructing the linearly
parameterized value function. Predictor-Corrector Temporal Difference (PCTD) is
what I call the translated discrete time Reinforcement Learning(RL) algorithm
from the continuous time ODE using the theory of Stochastic Approximation(SA).
Both causal and non-causal implementations of the algorithm are provided, and
simulation results are listed for an infinite horizon task to compare the
original TD(0) algorithm against both versions of PCTD(0). | [
"cs.LG",
"cs.AI"
] |
Arrhythmia detection from ECG is an important research subject in the
prevention and diagnosis of cardiovascular diseases. The prevailing studies
formulate arrhythmia detection from ECG as a time series classification
problem. Meanwhile, early detection of arrhythmia presents a real-world demand
for early prevention and diagnosis. In this paper, we address a problem of
cardiovascular disease early classification, which is a varied-length and
long-length time series early classification problem as well. For solving this
problem, we propose a deep reinforcement learning-based framework, namely
Snippet Policy Network (SPN), consisting of four modules, snippet generator,
backbone network, controlling agent, and discriminator. Comparing to the
existing approaches, the proposed framework features flexible input length,
solves the dual-optimization solution of the earliness and accuracy goals.
Experimental results demonstrate that SPN achieves an excellent performance of
over 80\% in terms of accuracy. Compared to the state-of-the-art methods, at
least 7% improvement on different metrics, including the precision, recall,
F1-score, and harmonic mean, is delivered by the proposed SPN. To the best of
our knowledge, this is the first work focusing on solving the cardiovascular
early classification problem based on varied-length ECG data. Based on these
excellent features from SPN, it offers a good exemplification for addressing
all kinds of varied-length time series early classification problems. | [
"cs.LG",
"eess.SP"
] |
Partial differential equations (PDEs) fitting scientific data can represent
physical laws with explainable mechanisms for various mathematically-oriented
subjects. Most natural dynamics are expressed by PDEs with varying coefficients
(PDEs-VC), which highlights the importance of PDE discovery. Previous
algorithms can discover some simple instances of PDEs-VC but fail in the
discovery of PDEs with coefficients of higher complexity, as a result of
coefficient estimation inaccuracy. In this paper, we propose KO-PDE, a kernel
optimized regression method that incorporates the kernel density estimation of
adjacent coefficients to reduce the coefficient estimation error. KO-PDE can
discover PDEs-VC on which previous baselines fail and is more robust against
inevitable noise in data. In experiments, the PDEs-VC of seven challenging
spatiotemporal scientific datasets in fluid dynamics are all discovered by
KO-PDE, while the three baselines render false results in most cases. With
state-of-the-art performance, KO-PDE sheds light on the automatic description
of natural phenomenons using discovered PDEs in the real world. | [
"cs.LG",
"cs.NA",
"math.NA"
] |
Stochastic gradient descent (SGD), which updates the model parameters by
adding a local gradient times a learning rate at each step, is widely used in
model training of machine learning algorithms such as neural networks. It is
observed that the models trained by SGD are sensitive to learning rates and
good learning rates are problem specific. We propose an algorithm to
automatically learn learning rates using neural network based actor-critic
methods from deep reinforcement learning (RL).In particular, we train a policy
network called actor to decide the learning rate at each step during training,
and a value network called critic to give feedback about quality of the
decision (e.g., the goodness of the learning rate outputted by the actor) that
the actor made. The introduction of auxiliary actor and critic networks helps
the main network achieve better performance. Experiments on different datasets
and network architectures show that our approach leads to better convergence of
SGD than human-designed competitors. | [
"cs.LG"
] |
In this report we propose a classification technique for skin lesion images
as a part of our submission for ISIC 2018 Challenge in Skin Lesion Analysis
Towards Melanoma Detection. Our data was extracted from the ISIC 2018: Skin
Lesion Analysis Towards Melanoma Detection grand challenge datasets. The
features are extracted through a Convolutional Neural Network, in our case
ResNet50 and then using these features we train a DeepForest, having cascading
layers, to classify our skin lesion images. We know that Convolutional Neural
Networks are a state-of-the-art technique in representation learning for
images, with the convolutional filters learning to detect features from images
through backpropagation. These features are then usually fed to a classifier
like a softmax layer or other such classifiers for classification tasks. In our
case we do not use the traditional backpropagation method and train a softmax
layer for classification. Instead, we use Deep Forest, a novel decision tree
ensemble approach with performance highly competitive to deep neural networks
in a broad range of tasks. Thus we use a ResNet50 to extract the features from
skin lesion images and then use the Deep Forest to classify these images. This
method has been used because Deep Forest has been found to be hugely efficient
in areas where there are only small-scale training data available. Also as the
Deep Forest network decides its complexity by itself, it also caters to the
problem of dataset imbalance we faced in this problem. | [
"cs.CV"
] |
As an essential problem in computer vision, salient object detection (SOD)
has attracted an increasing amount of research attention over the years. Recent
advances in SOD are predominantly led by deep learning-based solutions (named
deep SOD). To enable in-depth understanding of deep SOD, in this paper, we
provide a comprehensive survey covering various aspects, ranging from algorithm
taxonomy to unsolved issues. In particular, we first review deep SOD algorithms
from different perspectives, including network architecture, level of
supervision, learning paradigm, and object-/instance-level detection. Following
that, we summarize and analyze existing SOD datasets and evaluation metrics.
Then, we benchmark a large group of representative SOD models, and provide
detailed analyses of the comparison results. Moreover, we study the performance
of SOD algorithms under different attribute settings, which has not been
thoroughly explored previously, by constructing a novel SOD dataset with rich
attribute annotations covering various salient object types, challenging
factors, and scene categories. We further analyze, for the first time in the
field, the robustness of SOD models to random input perturbations and
adversarial attacks. We also look into the generalization and difficulty of
existing SOD datasets. Finally, we discuss several open issues of SOD and
outline future research directions. | [
"cs.CV"
] |
Vision based object grasping and manipulation in robotics require accurate
estimation of object's 6D pose. The 6D pose estimation has received significant
attention in computer vision community and multiple datasets and evaluation
metrics have been proposed. However, the existing metrics measure how well two
geometrical surfaces are aligned - ground truth vs. estimated pose - which does
not directly measure how well a robot can perform the task with the given
estimate. In this work we propose a probabilistic metric that directly measures
success in robotic tasks. The evaluation metric is based on non-parametric
probability density that is estimated from samples of a real physical setup.
During the pose evaluation stage the physical setup is not needed. The
evaluation metric is validated in controlled experiments and a new pose
estimation dataset of industrial parts is introduced. The experimental results
with the parts confirm that the proposed evaluation metric better reflects the
true performance in robotics than the existing metrics. | [
"cs.CV"
] |
Understanding generalization in reinforcement learning (RL) is a significant
challenge, as many common assumptions of traditional supervised learning theory
do not apply. We focus on the special class of reparameterizable RL problems,
where the trajectory distribution can be decomposed using the reparametrization
trick. For this problem class, estimating the expected return is efficient and
the trajectory can be computed deterministically given peripheral random
variables, which enables us to study reparametrizable RL using supervised
learning and transfer learning theory. Through these relationships, we derive
guarantees on the gap between the expected and empirical return for both
intrinsic and external errors, based on Rademacher complexity as well as the
PAC-Bayes bound. Our bound suggests the generalization capability of
reparameterizable RL is related to multiple factors including "smoothness" of
the environment transition, reward and agent policy function class. We also
empirically verify the relationship between the generalization gap and these
factors through simulations. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Automatic detection of animals that have strayed into human inhabited areas
has important security and road safety applications. This paper attempts to
solve this problem using deep learning techniques from a variety of computer
vision fields including object detection, tracking, segmentation and edge
detection. Several interesting insights into transfer learning are elicited
while adapting models trained on benchmark datasets for real world deployment.
Empirical evidence is presented to demonstrate the inability of detectors to
generalize from training images of animals in their natural habitats to
deployment scenarios of man-made environments. A solution is also proposed
using semi-automated synthetic data generation for domain specific training.
Code and data used in the experiments are made available to facilitate further
work in this domain. | [
"cs.CV"
] |
A Relational Markov Decision Process (RMDP) is a first-order representation
to express all instances of a single probabilistic planning domain with
possibly unbounded number of objects. Early work in RMDPs outputs generalized
(instance-independent) first-order policies or value functions as a means to
solve all instances of a domain at once. Unfortunately, this line of work met
with limited success due to inherent limitations of the representation space
used in such policies or value functions. Can neural models provide the missing
link by easily representing more complex generalized policies, thus making them
effective on all instances of a given domain?
We present SymNet, the first neural approach for solving RMDPs that are
expressed in the probabilistic planning language of RDDL. SymNet trains a set
of shared parameters for an RDDL domain using training instances from that
domain. For each instance, SymNet first converts it to an instance graph and
then uses relational neural models to compute node embeddings. It then scores
each ground action as a function over the first-order action symbols and node
embeddings related to the action. Given a new test instance from the same
domain, SymNet architecture with pre-trained parameters scores each ground
action and chooses the best action. This can be accomplished in a single
forward pass without any retraining on the test instance, thus implicitly
representing a neural generalized policy for the whole domain. Our experiments
on nine RDDL domains from IPPC demonstrate that SymNet policies are
significantly better than random and sometimes even more effective than
training a state-of-the-art deep reactive policy from scratch. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm
that exploits inter-modality relationships to transform large scientific
datasets into smaller, more well-powered and interpretable low-dimensional
spaces. SiMLR contributes a novel objective function for identifying joint
signal, regularization based on sparse matrices representing prior
within-modality relationships and an implementation that permits application to
joint reduction of large data matrices, each of which may have millions of
entries. We demonstrate that SiMLR outperforms closely related methods on
supervised learning problems in simulation data, a multi-omics cancer survival
prediction dataset and multiple modality neuroimaging datasets. Taken together,
this collection of results shows that SiMLR may be applied with default
parameters to joint signal estimation from disparate modalities and may yield
practically useful results in a variety of application domains. | [
"stat.ML",
"cs.LG"
] |
Successful fine-grained image classification methods learn subtle details
between visually similar (sub-)classes, but the problem becomes significantly
more challenging if the details are missing due to low resolution. Encouraged
by the recent success of Convolutional Neural Network (CNN) architectures in
image classification, we propose a novel resolution-aware deep model which
combines convolutional image super-resolution and convolutional fine-grained
classification into a single model in an end-to-end manner. Extensive
experiments on the Stanford Cars and Caltech-UCSD Birds 200-2011 benchmarks
demonstrate that the proposed model consistently performs better than
conventional convolutional net on classifying fine-grained object classes in
low-resolution images. | [
"cs.CV"
] |
As melanoma diagnoses increase across the US, automated efforts to identify
malignant lesions become increasingly of interest to the research community.
Segmentation of dermoscopic images is the first step in this process, thus
accuracy is crucial. Although techniques utilizing convolutional neural
networks have been used in the past for lesion segmentation, we present a
solution employing the recently published DeepLab 3, an atrous convolution
method for image segmentation. Although the results produced by this run are
not ideal, with a mean Jaccard index of 0.498, we believe that with further
adjustments and modifications to the compatibility with the DeepLab code and
with training on more powerful processing units, this method may achieve better
results in future trials. | [
"cs.CV"
] |
Generative adversarial networks (GANs) are increasingly attracting attention
in the computer vision, natural language processing, speech synthesis and
similar domains. However, evaluating the performance of GANs is still an open
and challenging problem. Existing evaluation metrics primarily measure the
dissimilarity between real and generated images using automated statistical
methods. They often require large sample sizes for evaluation and do not
directly reflect human perception of image quality. In this work, we introduce
an evaluation metric called Neuroscore, for evaluating the performance of GANs,
that more directly reflects psychoperceptual image quality through the
utilization of brain signals. Our results show that Neuroscore has superior
performance to the current evaluation metrics in that: (1) It is more
consistent with human judgment; (2) The evaluation process needs much smaller
numbers of samples; and (3) It is able to rank the quality of images on a per
GAN basis. A convolutional neural network (CNN) based neuro-AI interface is
proposed to predict Neuroscore from GAN-generated images directly without the
need for neural responses. Importantly, we show that including neural responses
during the training phase of the network can significantly improve the
prediction capability of the proposed model. Codes and data can be referred at
this link: https://github.com/villawang/Neuro-AI-Interface. | [
"cs.CV",
"cs.HC",
"cs.LG",
"eess.IV"
] |
Computational intelligence-based ocean characteristics forecasting
applications, such as Significant Wave Height (SWH) prediction, are crucial for
avoiding social and economic loss in coastal cities. Compared to the
traditional empirical-based or numerical-based forecasting models, "soft
computing" approaches, including machine learning and deep learning models,
have shown numerous success in recent years. In this paper, we focus on
enabling the deep learning model to learn both short-term and long-term
spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural
Network (WGNN) approach is proposed to integrate the advantages of wavelet
transform and graph neural network. Several parallel graph neural networks are
separately trained on wavelet decomposed data, and the reconstruction of each
model's prediction forms the final SWH prediction. Experimental results show
that the proposed WGNN approach outperforms other models, including the
numerical models, the machine learning models, and several deep learning
models. | [
"cs.LG",
"cs.AI"
] |
Clustering is an unsupervised machine learning method grouping data samples
into clusters of similar objects. In practice, clustering has been used in
numerous applications such as banking customers profiling, document retrieval,
image segmentation, and e-commerce recommendation engines. However, the
existing clustering techniques present significant limitations, from which is
the dependability of their stability on the initialization parameters (e.g.
number of clusters, centroids). Different solutions were presented in the
literature to overcome this limitation (i.e. internal and external validation
metrics). However, these solutions require high computational complexity and
memory consumption, especially when dealing with big data. In this paper, we
apply the recent object detection Deep Learning (DL) model, named YOLO-v5, to
detect the initial clustering parameters such as the number of clusters with
their sizes and centroids. Mainly, the proposed solution consists of adding a
DL-based initialization phase making the clustering algorithms free of
initialization. Two model solutions are provided in this work, one for isolated
clusters and the other one for overlapping clusters. The features of the
incoming dataset determine which model to use. Moreover, The results show that
the proposed solution can provide near-optimal clusters initialization
parameters with low computational and resources overhead compared to existing
solutions. | [
"cs.CV"
] |
We present a novel modular object detection convolutional neural network that
significantly improves the accuracy of object detection. The network consists
of two stages in a hierarchical structure. The first stage is a network that
detects general classes. The second stage consists of separate networks to
refine the classification and localization of each of the general classes
objects. Compared to a state of the art object detection networks the
classification error in the modular network is improved by approximately 3-5
times, from 12% to 2.5 %-4.5%. This network is easy to implement and has a 0.94
mAP. The network architecture can be a platform to improve the accuracy of
widespread state of the art object detection networks and other kinds of deep
learning networks. We show that a deep learning network initialized by transfer
learning becomes more accurate as the number of classes it later trained to
detect becomes smaller. | [
"cs.CV",
"cs.LG",
"cs.NE",
"eess.IV",
"I.2.10; I.2.10; I.2.1; I.4.6; I.4.7; I.4.8; I.4.9; I.5.2; I.5.5"
] |
We introduce PowerGym, an open-source reinforcement learning environment for
Volt-Var control in power distribution systems. Following OpenAI Gym APIs,
PowerGym targets minimizing power loss and voltage violations under physical
networked constraints. PowerGym provides four distribution systems (13Bus,
34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design
variants for various control difficulties. To foster generalization, PowerGym
offers a detailed customization guide for users working with their distribution
systems. As a demonstration, we examine state-of-the-art reinforcement learning
algorithms in PowerGym and validate the environment by studying controller
behaviors. The repository is available at
\url{https://github.com/siemens/powergym}. | [
"cs.LG",
"cs.AI"
] |
This paper presents a new neural architecture that combines a modulated
Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network
architecture (MOHQA). The hypothesis is that such a combination allows MOHQA to
solve difficult partially observable Markov decision process (POMDP) problems
which impair temporal difference (TD)-based RL algorithms such as DQN, as the
TD error cannot be easily derived from observations. The key idea is to use a
Hebbian network with bio-inspired neural traces in order to bridge temporal
delays between actions and rewards when confounding observations and sparse
rewards result in inaccurate TD errors. In MOHQA, DQN learns low level features
and control, while the MOHN contributes to the high-level decisions by
associating rewards with past states and actions. Thus the proposed
architecture combines two modules with significantly different learning
algorithms, a Hebbian associative network and a classical DQN pipeline,
exploiting the advantages of both. Simulations on a set of POMDPs and on the
MALMO environment show that the proposed algorithm improved DQN's results and
even outperformed control tests with A2C, QRDQN+LSTM and REINFORCE algorithms
on some POMDPs with confounding stimuli and sparse rewards. | [
"cs.LG",
"stat.ML"
] |
The use of deep 3D point cloud models in safety-critical applications, such
as autonomous driving, dictates the need to certify the robustness of these
models to real-world transformations. This is technically challenging, as it
requires a scalable verifier tailored to point cloud models that handles a wide
range of semantic 3D transformations. In this work, we address this challenge
and introduce 3DCertify, the first verifier able to certify the robustness of
point cloud models. 3DCertify is based on two key insights: (i) a generic
relaxation based on first-order Taylor approximations, applicable to any
differentiable transformation, and (ii) a precise relaxation for global feature
pooling, which is more complex than pointwise activations (e.g., ReLU or
sigmoid) but commonly employed in point cloud models. We demonstrate the
effectiveness of 3DCertify by performing an extensive evaluation on a wide
range of 3D transformations (e.g., rotation, twisting) for both classification
and part segmentation tasks. For example, we can certify robustness against
rotations by $\pm$60{\deg} for 95.7% of point clouds, and our max pool
relaxation increases certification by up to 15.6%. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Most state-of-the-art person re-identification (re-id) methods depend on
supervised model learning with a large set of cross-view identity labelled
training data. Even worse, such trained models are limited to only the
same-domain deployment with significantly degraded cross-domain generalization
capability, i.e. "domain specific". To solve this limitation, there are a
number of recent unsupervised domain adaptation and unsupervised learning
methods that leverage unlabelled target domain training data. However, these
methods need to train a separate model for each target domain as supervised
learning methods. This conventional "{\em train once, run once}" pattern is
unscalable to a large number of target domains typically encountered in
real-world deployments. We address this problem by presenting a "train once,
run everywhere" pattern industry-scale systems are desperate for. We formulate
a "universal model learning' approach enabling domain-generic person re-id
using only limited training data of a "{\em single}" seed domain. Specifically,
we train a universal re-id deep model to discriminate between a set of
transformed person identity classes. Each of such classes is formed by applying
a variety of random appearance transformations to the images of that class,
where the transformations simulate the camera viewing conditions of any domains
for making the model training domain generic. Extensive evaluations show the
superiority of our method for universal person re-id over a wide variety of
state-of-the-art unsupervised domain adaptation and unsupervised learning re-id
methods on five standard benchmarks: Market-1501, DukeMTMC, CUHK03, MSMT17, and
VIPeR. | [
"cs.CV"
] |
The training of Generative Adversarial Networks (GANs) requires a large
amount of data, stimulating the development of new data augmentation methods to
alleviate the challenge. Oftentimes, these methods either fail to produce
enough new data or expand the dataset beyond the original knowledge domain. In
this paper, we propose a new way of representing the available knowledge in the
manifold of data barycenters. Such a representation allows performing data
augmentation based on interpolation between the nearest data elements using
Wasserstein distance. The proposed method finds cliques in the
nearest-neighbors graph and, at each sampling iteration, randomly draws one
clique to compute the Wasserstein barycenter with random uniform weights. These
barycenters then become the new natural-looking elements that one could add to
the dataset. We apply this approach to the problem of landmarks detection and
augment the available landmarks data within the dataset. Additionally, the idea
is validated on cardiac data for the task of medical segmentation. Our approach
reduces the overfitting and improves the quality metrics both beyond the
original data outcome and beyond the result obtained with classical
augmentation methods. | [
"cs.CV"
] |
Predicting the future can significantly improve the safety of intelligent
vehicles, which is a key component in autonomous driving. 3D point clouds
accurately model 3D information of surrounding environment and are crucial for
intelligent vehicles to perceive the scene. Therefore, prediction of 3D point
clouds has great significance for intelligent vehicles, which can be utilized
for numerous further applications. However, due to point clouds are unordered
and unstructured, point cloud prediction is challenging and has not been deeply
explored in current literature. In this paper, we propose a novel motion-based
neural network named MoNet. The key idea of the proposed MoNet is to integrate
motion features between two consecutive point clouds into the prediction
pipeline. The introduction of motion features enables the model to more
accurately capture the variations of motion information across frames and thus
make better predictions for future motion. In addition, content features are
introduced to model the spatial content of individual point clouds. A recurrent
neural network named MotionRNN is proposed to capture the temporal correlations
of both features. Besides, we propose an attention-based motion align module to
address the problem of missing motion features in the inference pipeline.
Extensive experiments on two large scale outdoor LiDAR datasets demonstrate the
performance of the proposed MoNet. Moreover, we perform experiments on
applications using the predicted point clouds and the results indicate the
great application potential of the proposed method. | [
"cs.CV"
] |
As edge devices become prevalent, deploying Deep Neural Networks (DNN) on
edge devices has become a critical issue. However, DNN requires a high
computational resource which is rarely available for edge devices. To handle
this, we propose a novel model compression method for the devices with limited
computational resources, called PQK consisting of pruning, quantization, and
knowledge distillation (KD) processes. Unlike traditional pruning and KD, PQK
makes use of unimportant weights pruned in the pruning process to make a
teacher network for training a better student network without pre-training the
teacher model. PQK has two phases. Phase 1 exploits iterative pruning and
quantization-aware training to make a lightweight and power-efficient model. In
phase 2, we make a teacher network by adding unimportant weights unused in
phase 1 to a pruned network. By using this teacher network, we train the pruned
network as a student network. In doing so, we do not need a pre-trained teacher
network for the KD framework because the teacher and the student networks
coexist within the same network. We apply our method to the recognition model
and verify the effectiveness of PQK on keyword spotting (KWS) and image
recognition. | [
"cs.LG"
] |
This paper proposes the first known to us iris recognition methodology
designed specifically for post-mortem samples. We propose to use deep
learning-based iris segmentation models to extract highly irregular iris
texture areas in post-mortem iris images. We show how to use segmentation masks
predicted by neural networks in conventional, Gabor-based iris recognition
method, which employs circular approximations of the pupillary and limbic iris
boundaries. As a whole, this method allows for a significant improvement in
post-mortem iris recognition accuracy over the methods designed only for
ante-mortem irises, including the academic OSIRIS and commercial IriCore
implementations. The proposed method reaches the EER less than 1% for samples
collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER
observed for OSIRIS and IriCore, respectively. For samples collected up to 369
hours post-mortem, the proposed method achieves the EER 21.45%, while 33.59%
and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the
method is tested on a database of iris images collected from ophthalmology
clinic patients, for which it also offers an advantage over the two other
algorithms. This work is the first step towards post-mortem-specific iris
recognition, which increases the chances of identification of deceased subjects
in forensic investigations. The new database of post-mortem iris images
acquired from 42 subjects, as well as the deep learning-based segmentation
models are made available along with the paper, to ensure all the results
presented in this manuscript are reproducible. | [
"cs.CV"
] |
Knowledge distillation methods are proved to be promising in improving the
performance of neural networks and no additional computational expenses are
required during the inference time. For the sake of boosting the accuracy of
object detection, a great number of knowledge distillation methods have been
proposed particularly designed for object detection. However, most of these
methods only focus on feature-level distillation and label-level distillation,
leaving the label assignment step, a unique and paramount procedure for object
detection, by the wayside. In this work, we come up with a simple but effective
knowledge distillation approach focusing on label assignment in object
detection, in which the positive and negative samples of student network are
selected in accordance with the predictions of teacher network. Our method
shows encouraging results on the MSCOCO2017 benchmark, and can not only be
applied to both one-stage detectors and two-stage detectors but also be
utilized orthogonally with other knowledge distillation methods. | [
"cs.CV",
"cs.AI"
] |
Registration is a fundamental but critical task in point cloud processing,
which usually depends on finding element correspondence from two point clouds.
However, the finding of reliable correspondence relies on establishing a robust
and discriminative description of elements and the correct matching of
corresponding elements. In this letter, we develop a coarse-to-fine
registration strategy, which utilizes rotation-invariant features and a new
weighted graph matching method for iteratively finding correspondence. In the
graph matching method, the similarity of nodes and edges in Euclidean and
feature space are formulated to construct the optimization function. The
proposed strategy is evaluated using two benchmark datasets and compared with
several state-of-the-art methods. Regarding the experimental results, our
proposed method can achieve a fine registration with rotation errors of less
than 0.2 degrees and translation errors of less than 0.1m. | [
"cs.CV"
] |
The recent advancements in machine learning (ML) have demonstrated the
potential for providing a powerful solution to build complex prediction systems
in a short time. However, in highly regulated industries, such as the financial
technology (Fintech), people have raised concerns about the risk of ML systems
discriminating against specific protected groups or individuals. To address
these concerns, researchers have introduced various mathematical fairness
metrics and bias mitigation algorithms. This paper discusses hidden technical
debts and challenges of building fair ML systems in a production environment
for Fintech. We explore various stages that require attention for fairness in
the ML system development and deployment life cycle. To identify hidden
technical debts that exist in building fair ML system for Fintech, we focus on
key pipeline stages including data preparation, model development, system
monitoring and integration in production. Our analysis shows that enforcing
fairness for production-ready ML systems in Fintech requires specific
engineering commitments at different stages of ML system life cycle. We also
propose several initial starting points to mitigate these technical debts for
deploying fair ML systems in production. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Spatial Transformer Networks (STN) can generate geometric transformations
which modify input images to improve the classifier's performance. In this
work, we combine the idea of STN with Reinforcement Learning (RL). To this end,
we break the affine transformation down into a sequence of simple and discrete
transformations. We formulate the task as a Markovian Decision Process (MDP)
and use RL to solve this sequential decision-making problem. STN architectures
learn the transformation parameters by minimizing the classification error and
backpropagating the gradients through a sub-differentiable sampling module. In
our method, we are not bound to the differentiability of the sampling modules.
Moreover, we have freedom in designing the objective rather than only
minimizing the error; e.g., we can directly set the target as maximizing the
accuracy. We design multiple experiments to verify the effectiveness of our
method using cluttered MNIST and Fashion-MNIST datasets and show that our
method outperforms STN with a proper definition of MDP components. | [
"cs.LG"
] |
Visual correspondence is a fundamental building block on the way to building
assistive tools for hand-drawn animation. However, while a large body of work
has focused on learning visual correspondences at the pixel-level, few
approaches have emerged to learn correspondence at the level of line enclosures
(segments) that naturally occur in hand-drawn animation. Exploiting this
structure in animation has numerous benefits: it avoids the intractable memory
complexity of attending to individual pixels in high resolution images and
enables the use of real-world animation datasets that contain correspondence
information at the level of per-segment colors. To that end, we propose the
Animation Transformer (AnT) which uses a transformer-based architecture to
learn the spatial and visual relationships between segments across a sequence
of images. AnT enables practical ML-assisted colorization for professional
animation workflows and is publicly accessible as a creative tool in Cadmium. | [
"cs.CV",
"cs.AI",
"cs.GR"
] |
We introduce general scattering transforms as mathematical models of deep
neural networks with l2 pooling. Scattering networks iteratively apply complex
valued unitary operators, and the pooling is performed by a complex modulus. An
expected scattering defines a contractive representation of a high-dimensional
probability distribution, which preserves its mean-square norm. We show that
unsupervised learning can be casted as an optimization of the space contraction
to preserve the volume occupied by unlabeled examples, at each layer of the
network. Supervised learning and classification are performed with an averaged
scattering, which provides scattering estimations for multiple classes. | [
"cs.LG",
"stat.ML"
] |
Although significant progress has been made in synthesizing high-quality and
visually realistic face images by unconditional Generative Adversarial Networks
(GANs), there still lacks of control over the generation process in order to
achieve semantic face editing. In addition, it remains very challenging to
maintain other face information untouched while editing the target attributes.
In this paper, we propose a novel learning framework, called GuidedStyle, to
achieve semantic face editing on StyleGAN by guiding the image generation
process with a knowledge network. Furthermore, we allow an attention mechanism
in StyleGAN generator to adaptively select a single layer for style
manipulation. As a result, our method is able to perform disentangled and
controllable edits along various attributes, including smiling, eyeglasses,
gender, mustache and hair color. Both qualitative and quantitative results
demonstrate the superiority of our method over other competing methods for
semantic face editing. Moreover, we show that our model can be also applied to
different types of real and artistic face editing, demonstrating strong
generalization ability. | [
"cs.CV"
] |
Robust Optimization is becoming increasingly important in machine learning
applications. This paper studies the problem of robust submodular minimization
subject to combinatorial constraints. Constrained Submodular Minimization
arises in several applications such as co-operative cuts in image segmentation,
co-operative matchings in image correspondence, etc. Many of these models are
defined over clusterings of data points (for example pixels in images), and it
is important for these models to be robust to perturbations and uncertainty in
the data. While several existing papers have studied robust submodular
maximization, ours is the first work to study the minimization version under a
broad range of combinatorial constraints including cardinality, knapsack,
matroid as well as graph-based constraints such as cuts, paths, matchings, and
trees. In each case, we provide scalable approximation algorithms and also
study hardness bounds. Finally, we empirically demonstrate the utility of our
algorithms on synthetic and real-world datasets. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
A challenging open question in deep learning is how to handle tabular data.
Unlike domains such as image and natural language processing, where deep
architectures prevail, there is still no widely accepted neural architecture
that dominates tabular data. As a step toward bridging this gap, we present
DNF-Net a novel generic architecture whose inductive bias elicits models whose
structure corresponds to logical Boolean formulas in disjunctive normal form
(DNF) over affine soft-threshold decision terms. In addition, DNF-Net promotes
localized decisions that are taken over small subsets of the features. We
present an extensive empirical study showing that DNF-Nets significantly and
consistently outperform FCNs over tabular data. With relatively few
hyperparameters, DNF-Nets open the door to practical end-to-end handling of
tabular data using neural networks. We present ablation studies, which justify
the design choices of DNF-Net including the three inductive bias elements,
namely, Boolean formulation, locality, and feature selection. | [
"cs.LG",
"stat.ML"
] |
We propose a universal image reconstruction method to represent detailed
images purely from binary sparse edge and flat color domain. Inspired by the
procedures of painting, our framework, based on generative adversarial network,
consists of three phases: Imitation Phase aims at initializing networks,
followed by Generating Phase to reconstruct preliminary images. Moreover,
Refinement Phase is utilized to fine-tune preliminary images into final outputs
with details. This framework allows our model generating abundant high
frequency details from sparse input information. We also explore the defects of
disentangling style latent space implicitly from images, and demonstrate that
explicit color domain in our model performs better on controllability and
interpretability. In our experiments, we achieve outstanding results on
reconstructing realistic images and translating hand drawn drafts into
satisfactory paintings. Besides, within the domain of edge-to-image
translation, our model PI-REC outperforms existing state-of-the-art methods on
evaluations of realism and accuracy, both quantitatively and qualitatively. | [
"cs.CV"
] |
We present a single-image 3D face synthesis technique that can handle
challenging facial expressions while recovering fine geometric details. Our
technique employs expression analysis for proxy face geometry generation and
combines supervised and unsupervised learning for facial detail synthesis. On
proxy generation, we conduct emotion prediction to determine a new
expression-informed proxy. On detail synthesis, we present a Deep Facial Detail
Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs
both geometry and appearance loss functions. For geometry, we capture 366
high-quality 3D scans from 122 different subjects under 3 facial expressions.
For appearance, we use additional 20K in-the-wild face images and apply
image-based rendering to accommodate lighting variations. Comprehensive
experiments demonstrate that our framework can produce high-quality 3D faces
with realistic details under challenging facial expressions. | [
"cs.CV"
] |
While radar and video data can be readily fused at the detection level,
fusing them at the pixel level is potentially more beneficial. This is also
more challenging in part due to the sparsity of radar, but also because
automotive radar beams are much wider than a typical pixel combined with a
large baseline between camera and radar, which results in poor association
between radar pixels and color pixel. A consequence is that depth completion
methods designed for LiDAR and video fare poorly for radar and video. Here we
propose a radar-to-pixel association stage which learns a mapping from radar
returns to pixels. This mapping also serves to densify radar returns. Using
this as a first stage, followed by a more traditional depth completion method,
we are able to achieve image-guided depth completion with radar and video. We
demonstrate performance superior to camera and radar alone on the nuScenes
dataset. Our source code is available at https://github.com/longyunf/rc-pda. | [
"cs.CV"
] |
Recent researches show that machine learning has the potential to learn
better heuristics than the one designed by human for solving combinatorial
optimization problems. The deep neural network is used to characterize the
input instance for constructing a feasible solution incrementally. Recently, an
attention model is proposed to solve routing problems. In this model, the state
of an instance is represented by node features that are fixed over time.
However, the fact is, the state of an instance is changed according to the
decision that the model made at different construction steps, and the node
features should be updated correspondingly. Therefore, this paper presents a
dynamic attention model with dynamic encoder-decoder architecture, which
enables the model to explore node features dynamically and exploit hidden
structure information effectively at different construction steps. This paper
focuses on a challenging NP-hard problem, vehicle routing problem. The
experiments indicate that our model outperforms the previous methods and also
shows a good generalization performance. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
This paper describes InfoCatVAE, an extension of the variational autoencoder
that enables unsupervised disentangled representation learning. InfoCatVAE uses
multimodal distributions for the prior and the inference network and then
maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
derived for our model with a natural soft clustering objective which explains
the robustness of our approach. We then adapt the InfoGANs method to our
setting in order to maximize the mutual information between the categorical
code and the generated inputs and obtain an improved model. | [
"cs.LG",
"stat.ML"
] |
While CNNs naturally lend themselves to densely sampled data, and
sophisticated implementations are available, they lack the ability to
efficiently process sparse data. In this work we introduce a suite of tools
that exploit sparsity in both the feature maps and the filter weights, and
thereby allow for significantly lower memory footprints and computation times
than the conventional dense framework when processing data with a high degree
of sparsity. Our scheme provides (i) an efficient GPU implementation of a
convolution layer based on direct, sparse convolution; (ii) a filter step
within the convolution layer, which we call attention, that prevents fill-in,
i.e., the tendency of convolution to rapidly decrease sparsity, and guarantees
an upper bound on the computational resources; and (iii) an adaptation of the
back-propagation algorithm, which makes it possible to combine our approach
with standard learning frameworks, while still exploiting sparsity in the data
and the model. | [
"cs.CV"
] |
Salient object detection is a fundamental topic in computer vision. Previous
methods based on RGB-D often suffer from the incompatibility of multi-modal
feature fusion and the insufficiency of multi-scale feature aggregation. To
tackle these two dilemmas, we propose a novel multi-modal and multi-scale
refined network (M2RNet). Three essential components are presented in this
network. The nested dual attention module (NDAM) explicitly exploits the
combined features of RGB and depth flows. The adjacent interactive aggregation
module (AIAM) gradually integrates the neighbor features of high, middle and
low levels. The joint hybrid optimization loss (JHOL) makes the predictions
have a prominent outline. Extensive experiments demonstrate that our method
outperforms other state-of-the-art approaches. | [
"cs.CV"
] |
Scene labeling is the problem of assigning an object label to each pixel. It
unifies the image segmentation and object recognition problems. The importance
of using contextual information in scene labeling frameworks has been widely
realized in the field. We propose a contextual framework, called contextual
hierarchical model (CHM), which learns contextual information in a hierarchical
framework for scene labeling. At each level of the hierarchy, a classifier is
trained based on downsampled input images and outputs of previous levels. Our
model then incorporates the resulting multi-resolution contextual information
into a classifier to segment the input image at original resolution. This
training strategy allows for optimization of a joint posterior probability at
multiple resolutions through the hierarchy. Contextual hierarchical model is
purely based on the input image patches and does not make use of any fragments
or shape examples. Hence, it is applicable to a variety of problems such as
object segmentation and edge detection. We demonstrate that CHM outperforms
state-of-the-art on Stanford background and Weizmann horse datasets. It also
outperforms state-of-the-art edge detection methods on NYU depth dataset and
achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500). | [
"cs.CV"
] |
This work introduces Bilinear Classes, a new structural framework, which
permit generalization in reinforcement learning in a wide variety of settings
through the use of function approximation. The framework incorporates nearly
all existing models in which a polynomial sample complexity is achievable, and,
notably, also includes new models, such as the Linear $Q^*/V^*$ model in which
both the optimal $Q$-function and the optimal $V$-function are linear in some
known feature space. Our main result provides an RL algorithm which has
polynomial sample complexity for Bilinear Classes; notably, this sample
complexity is stated in terms of a reduction to the generalization error of an
underlying supervised learning sub-problem. These bounds nearly match the best
known sample complexity bounds for existing models. Furthermore, this framework
also extends to the infinite dimensional (RKHS) setting: for the the Linear
$Q^*/V^*$ model, linear MDPs, and linear mixture MDPs, we provide sample
complexities that have no explicit dependence on the explicit feature dimension
(which could be infinite), but instead depends only on information theoretic
quantities. | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] |
In this paper, we propose a novel query design for the transformer-based
detectors. In previous transformer-based detectors, the object queries are a
set of learned embeddings. However, each learned embedding does not have an
explicit physical meaning and we can not explain where it will focus on. It is
difficult to optimize as the prediction slot of each object query does not have
a specific mode. In other words, each object query will not focus on a specific
region. To solved these problems, in our query design, object queries are based
on anchor points, which are widely used in CNN-based detectors. So each object
query focus on the objects near the anchor point. Moreover, our query design
can predict multiple objects at one position to solve the difficulty: "one
region, multiple objects". In addition, we design an attention variant, which
can reduce the memory cost while achieving similar or better performance than
the standard attention in DETR. Thanks to the query design and the attention
variant, the proposed detector that we called Anchor DETR, can achieve better
performance and run faster than the DETR with 10$\times$ fewer training epochs.
For example, it achieves 44.2 AP with 16 FPS on the MSCOCO dataset when using
the ResNet50-DC5 feature for training 50 epochs. Extensive experiments on the
MSCOCO benchmark prove the effectiveness of the proposed methods. Code is
available at https://github.com/megvii-model/AnchorDETR. | [
"cs.CV"
] |
Binary neural networks (BNNs) have received increasing attention due to their
superior reductions of computation and memory. Most existing works focus on
either lessening the quantization error by minimizing the gap between the
full-precision weights and their binarization or designing a gradient
approximation to mitigate the gradient mismatch, while leaving the "dead
weights" untouched. This leads to slow convergence when training BNNs. In this
paper, for the first time, we explore the influence of "dead weights" which
refer to a group of weights that are barely updated during the training of
BNNs, and then introduce rectified clamp unit (ReCU) to revive the "dead
weights" for updating. We prove that reviving the "dead weights" by ReCU can
result in a smaller quantization error. Besides, we also take into account the
information entropy of the weights, and then mathematically analyze why the
weight standardization can benefit BNNs. We demonstrate the inherent
contradiction between minimizing the quantization error and maximizing the
information entropy, and then propose an adaptive exponential scheduler to
identify the range of the "dead weights". By considering the "dead weights",
our method offers not only faster BNN training, but also state-of-the-art
performance on CIFAR-10 and ImageNet, compared with recent methods. Code can be
available at https://github.com/z-hXu/ReCU. | [
"cs.LG",
"cs.CV"
] |
A family of super deep networks, referred to as residual networks or ResNet,
achieved record-beating performance in various visual tasks such as image
recognition, object detection, and semantic segmentation. The ability to train
very deep networks naturally pushed the researchers to use enormous resources
to achieve the best performance. Consequently, in many applications super deep
residual networks were employed for just a marginal improvement in performance.
In this paper, we propose epsilon-ResNet that allows us to automatically
discard redundant layers, which produces responses that are smaller than a
threshold epsilon, with a marginal or no loss in performance. The
epsilon-ResNet architecture can be achieved using a few additional rectified
linear units in the original ResNet. Our method does not use any additional
variables nor numerous trials like other hyper-parameter optimization
techniques. The layer selection is achieved using a single training process and
the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet
datasets. In some instances, we achieve about 80% reduction in the number of
parameters. | [
"cs.CV"
] |
With the onset of COVID-19 and the resulting shelter in place guidelines
combined with remote working practices, human mobility in 2020 has been
dramatically impacted. Existing studies typically examine whether mobility in
specific localities increases or decreases at specific points in time and
relate these changes to certain pandemic and policy events. In this paper, we
study mobility change in the US through a five-step process using mobility
footprint data. (Step 1) Propose the delta Time Spent in Public Places
(Delta-TSPP) as a measure to quantify daily changes in mobility for each US
county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to
reduce the Delta-TSPP time series of each county to lower-dimensional latent
components of change in mobility. (Step 3) Conduct clustering analysis to find
counties that exhibit similar latent components. (Step 4) Investigate local and
global spatial autocorrelation for each component. (Step 5) Conduct correlation
analysis to investigate how various population characteristics and behavior
correlate with mobility patterns. Results show that by describing each county
as a linear combination of the three latent components, we can explain 59% of
the variation in mobility trends across all US counties. Specifically, change
in mobility in 2020 for US counties can be explained as a combination of three
latent components: 1) long-term reduction in mobility, 2) no change in
mobility, and 3) short-term reduction in mobility. We observe significant
correlations between the three latent components of mobility change and various
population characteristics, including political leaning, population, COVID-19
cases and deaths, and unemployment. We find that our analysis provides a
comprehensive understanding of mobility change in response to the COVID-19
pandemic. | [
"cs.LG"
] |
The goal of supervised representation learning is to construct effective data
representations for prediction. Among all the characteristics of an ideal
nonparametric representation of high-dimensional complex data, sufficiency, low
dimensionality and disentanglement are some of the most essential ones. We
propose a deep dimension reduction approach to learning representations with
these characteristics. The proposed approach is a nonparametric generalization
of the sufficient dimension reduction method. We formulate the ideal
representation learning task as that of finding a nonparametric representation
that minimizes an objective function characterizing conditional independence
and promoting disentanglement at the population level. We then estimate the
target representation at the sample level nonparametrically using deep neural
networks. We show that the estimated deep nonparametric representation is
consistent in the sense that its excess risk converges to zero. Our extensive
numerical experiments using simulated and real benchmark data demonstrate that
the proposed methods have better performance than several existing dimension
reduction methods and the standard deep learning models in the context of
classification and regression. | [
"cs.LG",
"stat.ML"
] |
In this paper, we present Co-scale conv-attentional image Transformers
(CoaT), a Transformer-based image classifier equipped with co-scale and
conv-attentional mechanisms. First, the co-scale mechanism maintains the
integrity of Transformers' encoder branches at individual scales, while
allowing representations learned at different scales to effectively communicate
with each other; we design a series of serial and parallel blocks to realize
the co-scale mechanism. Second, we devise a conv-attentional mechanism by
realizing a relative position embedding formulation in the factorized attention
module with an efficient convolution-like implementation. CoaT empowers image
Transformers with enriched multi-scale and contextual modeling capabilities. On
ImageNet, relatively small CoaT models attain superior classification results
compared with similar-sized convolutional neural networks and image/vision
Transformers. The effectiveness of CoaT's backbone is also illustrated on
object detection and instance segmentation, demonstrating its applicability to
downstream computer vision tasks. | [
"cs.CV",
"cs.LG",
"cs.NE"
] |
As handwriting input becomes more prevalent, the large symbol inventory
required to support Chinese handwriting recognition poses unique challenges.
This paper describes how the Apple deep learning recognition system can
accurately handle up to 30,000 Chinese characters while running in real-time
across a range of mobile devices. To achieve acceptable accuracy, we paid
particular attention to data collection conditions, representativeness of
writing styles, and training regimen. We found that, with proper care, even
larger inventories are within reach. Our experiments show that accuracy only
degrades slowly as the inventory increases, as long as we use training data of
sufficient quality and in sufficient quantity. | [
"cs.CV"
] |
More transformer blocks with residual connections have recently achieved
impressive results on various tasks. To achieve better performance with fewer
trainable parameters, recent methods are proposed to go shallower by parameter
sharing or model compressing along with the depth. However, weak modeling
capacity limits their performance. Contrastively, going wider by inducing more
trainable matrixes and parameters would produce a huge model requiring advanced
parallelism to train and inference.
In this paper, we propose a parameter-efficient framework, going wider
instead of deeper. Specially, following existing works, we adapt parameter
sharing to compress along depth. But, such deployment would limit the
performance. To maximize modeling capacity, we scale along model width by
replacing feed-forward network (FFN) with mixture-of-experts (MoE). Across
transformer blocks, instead of sharing normalization layers, we propose to use
individual layernorms to transform various semantic representations in a more
parameter-efficient way. To evaluate our plug-and-run framework, we design
WideNet and conduct comprehensive experiments on popular computer vision and
natural language processing benchmarks. On ImageNet-1K, our best model
outperforms Vision Transformer (ViT) by $1.5\%$ with $0.72 \times$ trainable
parameters. Using $0.46 \times$ and $0.13 \times$ parameters, our WideNet can
still surpass ViT and ViT-MoE by $0.8\%$ and $2.1\%$, respectively. On four
natural language processing datasets, WideNet outperforms ALBERT by $1.8\%$ on
average and surpass BERT using factorized embedding parameterization by $0.8\%$
with fewer parameters. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
In this work, we present our various contributions to the objective of
building a decision support tool for the diagnosis of rare diseases. Our goal
is to achieve a state of knowledge where the uncertainty about the patient's
disease is below a predetermined threshold. We aim to reach such states while
minimizing the average number of medical tests to perform. In doing so, we take
into account the need, in many medical applications, to avoid, as much as
possible, any misdiagnosis. To solve this optimization task, we investigate
several reinforcement learning algorithm and make them operable in our
high-dimensional and sparse-reward setting. We also present a way to combine
expert knowledge, expressed as conditional probabilities, with real clinical
data. This is crucial because the scarcity of data in the field of rare
diseases prevents any approach based solely on clinical data. Finally we show
that it is possible to integrate the ontological information about symptoms
while remaining in our probabilistic reasoning. It enables our decision support
tool to process information given at different level of precision by the user. | [
"cs.LG",
"stat.ML"
] |
Deep reinforcement learning primarily focuses on learning behavior, usually
overlooking the fact that an agent's function is largely determined by form.
So, how should one go about finding a morphology fit for solving tasks in a
given environment? Current approaches that co-adapt morphology and behavior use
a specific task's reward as a signal for morphology optimization. However, this
often requires expensive policy optimization and results in task-dependent
morphologies that are not built to generalize. In this work, we propose a new
approach, Task-Agnostic Morphology Evolution (TAME), to alleviate both of these
issues. Without any task or reward specification, TAME evolves morphologies by
only applying randomly sampled action primitives on a population of agents.
This is accomplished using an information-theoretic objective that efficiently
ranks agents by their ability to reach diverse states in the environment and
the causality of their actions. Finally, we empirically demonstrate that across
2D, 3D, and manipulation environments TAME can evolve morphologies that match
the multi-task performance of those learned with task supervised algorithms.
Our code and videos can be found at
https://sites.google.com/view/task-agnostic-evolution. | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
As a widely deployed security scheme, text-based CAPTCHAs have become more
and more difficult to resist machine learning-based attacks. So far, many
researchers have conducted attacking research on text-based CAPTCHAs deployed
by different companies (such as Microsoft, Amazon, and Apple) and achieved
certain results.However, most of these attacks have some shortcomings, such as
poor portability of attack methods, requiring a series of data preprocessing
steps, and relying on large amounts of labeled CAPTCHAs. In this paper, we
propose an efficient and simple end-to-end attack method based on
cycle-consistent generative adversarial networks. Compared with previous
studies, our method greatly reduces the cost of data labeling. In addition,
this method has high portability. It can attack common text-based CAPTCHA
schemes only by modifying a few configuration parameters, which makes the
attack easier. Firstly, we train CAPTCHA synthesizers based on the cycle-GAN to
generate some fake samples. Basic recognizers based on the convolutional
recurrent neural network are trained with the fake data. Subsequently, an
active transfer learning method is employed to optimize the basic recognizer
utilizing tiny amounts of labeled real-world CAPTCHA samples. Our approach
efficiently cracked the CAPTCHA schemes deployed by 10 popular websites,
indicating that our attack is likely very general. Additionally, we analyzed
the current most popular anti-recognition mechanisms. The results show that the
combination of more anti-recognition mechanisms can improve the security of
CAPTCHA, but the improvement is limited. Conversely, generating more complex
CAPTCHAs may cost more resources and reduce the availability of CAPTCHAs. | [
"cs.CV"
] |
As financial services (FS) companies have experienced drastic technology
driven changes, the availability of new data streams provides the opportunity
for more comprehensive customer understanding. We propose Dynamic Customer
Embeddings (DCE), a framework that leverages customers' digital activity and a
wide range of financial context to learn dense representations of customers in
the FS industry. Our method examines customer actions and pageviews within a
mobile or web digital session, the sequencing of the sessions themselves, and
snapshots of common financial features across our organization at the time of
login. We test our customer embeddings using real world data in three
prediction problems: 1) the intent of a customer in their next digital session,
2) the probability of a customer calling the call centers after a session, and
3) the probability of a digital session to be fraudulent. DCE showed
performance lift in all three downstream problems. | [
"cs.LG",
"stat.ML"
] |
Reinforcement Learning (RL) agents typically learn memoryless
policies---policies that only consider the last observation when selecting
actions. Learning memoryless policies is efficient and optimal in fully
observable environments. However, some form of memory is necessary when RL
agents are faced with partial observability. In this paper, we study a
lightweight approach to tackle partial observability in RL. We provide the
agent with an external memory and additional actions to control what, if
anything, is written to the memory. At every step, the current memory state is
part of the agent's observation, and the agent selects a tuple of actions: one
action that modifies the environment and another that modifies the memory. When
the external memory is sufficiently expressive, optimal memoryless policies
yield globally optimal solutions. Unfortunately, previous attempts to use
external memory in the form of binary memory have produced poor results in
practice. Here, we investigate alternative forms of memory in support of
learning effective memoryless policies. Our novel forms of memory outperform
binary and LSTM-based memory in well-established partially observable domains. | [
"cs.LG",
"cs.AI"
] |
Today, the dominant paradigm for training neural networks involves minimizing
task loss on a large dataset. Using world knowledge to inform a model, and yet
retain the ability to perform end-to-end training remains an open question. In
this paper, we present a novel framework for introducing declarative knowledge
to neural network architectures in order to guide training and prediction. Our
framework systematically compiles logical statements into computation graphs
that augment a neural network without extra learnable parameters or manual
redesign. We evaluate our modeling strategy on three tasks: machine
comprehension, natural language inference, and text chunking. Our experiments
show that knowledge-augmented networks can strongly improve over baselines,
especially in low-data regimes. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Zero-shot learning aims to recognize instances of unseen classes, for which
no visual instance is available during training, by learning multimodal
relations between samples from seen classes and corresponding class semantic
representations. These class representations usually consist of either
attributes, which do not scale well to large datasets, or word embeddings,
which lead to poorer performance. A good trade-off could be to employ short
sentences in natural language as class descriptions. We explore different
solutions to use such short descriptions in a ZSL setting and show that while
simple methods cannot achieve very good results with sentences alone, a
combination of usual word embeddings and sentences can significantly outperform
current state-of-the-art. | [
"cs.CV",
"cs.MM"
] |
Object detection has made impressive progress in recent years with the help
of deep learning. However, state-of-the-art algorithms are both computation and
memory intensive. Though many lightweight networks are developed for a
trade-off between accuracy and efficiency, it is still a challenge to make it
practical on an embedded device. In this paper, we present a system-level
solution for efficient object detection on a heterogeneous embedded device. The
detection network is quantized to low bits and allows efficient implementation
with shift operators. In order to make the most of the benefits of low-bit
quantization, we design a dedicated accelerator with programmable logic. Inside
the accelerator, a hybrid dataflow is exploited according to the heterogeneous
property of different convolutional layers. We adopt a straightforward but
resource-friendly column-prior tiling strategy to map the computation-intensive
convolutional layers to the accelerator that can support arbitrary feature
size. Other operations can be performed on the low-power CPU cores, and the
entire system is executed in a pipelined manner. As a case study, we evaluate
our object detection system on a real-world surveillance video with input size
of 512x512, and it turns out that the system can achieve an inference speed of
18 fps at the cost of 6.9W (with display) with an mAP of 66.4 verified on the
PASCAL VOC 2012 dataset. | [
"cs.CV",
"cs.DC"
] |
We tackle the blackbox issue of deep neural networks in the settings of
reinforcement learning (RL) where neural agents learn towards maximizing reward
gains in an uncontrollable way. Such learning approach is risky when the
interacting environment includes an expanse of state space because it is then
almost impossible to foresee all unwanted outcomes and penalize them with
negative rewards beforehand. Unlike reverse analysis of learned neural features
from previous works, our proposed method \nj{tackles the blackbox issue by
encouraging} an RL policy network to learn interpretable latent features
through an implementation of a disentangled representation learning method.
Toward this end, our method allows an RL agent to understand self-efficacy by
distinguishing its influences from uncontrollable environmental factors, which
closely resembles the way humans understand their scenes. Our experimental
results show that the learned latent factors not only are interpretable, but
also enable modeling the distribution of entire visited state space with a
specific action condition. We have experimented that this characteristic of the
proposed structure can lead to ex post facto governance for desired behaviors
of RL agents. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
A neural network regularizer (e.g., weight decay) boosts performance by
explicitly penalizing the complexity of a network. In this paper, we penalize
inferior network activations -- feature embeddings -- which in turn regularize
the network's weights implicitly. We propose singular value maximization
(SVMax) to learn a more uniform feature embedding. The SVMax regularizer
supports both supervised and unsupervised learning. Our formulation mitigates
model collapse and enables larger learning rates. We evaluate the SVMax
regularizer using both retrieval and generative adversarial networks. We
leverage a synthetic mixture of Gaussians dataset to evaluate SVMax in an
unsupervised setting. For retrieval networks, SVMax achieves significant
improvement margins across various ranking losses. Code available at
https://bit.ly/3jNkgDt | [
"cs.CV",
"cs.LG"
] |
Firefighting is a dynamic activity, in which numerous operations occur
simultaneously. Maintaining situational awareness (i.e., knowledge of current
conditions and activities at the scene) is critical to the accurate
decision-making necessary for the safe and successful navigation of a fire
environment by firefighters. Conversely, the disorientation caused by hazards
such as smoke and extreme heat can lead to injury or even fatality. This
research implements recent advancements in technology such as deep learning,
point cloud and thermal imaging, and augmented reality platforms to improve a
firefighter's situational awareness and scene navigation through improved
interpretation of that scene. We have designed and built a prototype embedded
system that can leverage data streamed from cameras built into a firefighter's
personal protective equipment (PPE) to capture thermal, RGB color, and depth
imagery and then deploy already developed deep learning models to analyze the
input data in real time. The embedded system analyzes and returns the processed
images via wireless streaming, where they can be viewed remotely and relayed
back to the firefighter using an augmented reality platform that visualizes the
results of the analyzed inputs and draws the firefighter's attention to objects
of interest, such as doors and windows otherwise invisible through smoke and
flames. | [
"cs.CV",
"cs.AI"
] |
Recent years have witnessed the remarkable progress of applying deep learning
models in video person re-identification (Re-ID). A key factor for video person
Re-ID is to effectively construct discriminative and robust video feature
representations for many complicated situations. Part-based approaches employ
spatial and temporal attention to extract representative local features. While
correlations between parts are ignored in the previous methods, to leverage the
relations of different parts, we propose an innovative adaptive graph
representation learning scheme for video person Re-ID, which enables the
contextual interactions between relevant regional features. Specifically, we
exploit the pose alignment connection and the feature affinity connection to
construct an adaptive structure-aware adjacency graph, which models the
intrinsic relations between graph nodes. We perform feature propagation on the
adjacency graph to refine regional features iteratively, and the neighbor
nodes' information is taken into account for part feature representation. To
learn compact and discriminative representations, we further propose a novel
temporal resolution-aware regularization, which enforces the consistency among
different temporal resolutions for the same identities. We conduct extensive
evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and
DukeMTMC-VideoReID, experimental results achieve the competitive performance
which demonstrates the effectiveness of our proposed method. The code is
available at https://github.com/weleen/AGRL.pytorch. | [
"cs.CV"
] |
Learning with noisy labels has attracted a lot of attention in recent years,
where the mainstream approaches are in pointwise manners. Meanwhile, pairwise
manners have shown great potential in supervised metric learning and
unsupervised contrastive learning. Thus, a natural question is raised: does
learning in a pairwise manner mitigate label noise? To give an affirmative
answer, in this paper, we propose a framework called Class2Simi: it transforms
data points with noisy class labels to data pairs with noisy similarity labels,
where a similarity label denotes whether a pair shares the class label or not.
Through this transformation, the reduction of the noise rate is theoretically
guaranteed, and hence it is in principle easier to handle noisy similarity
labels. Amazingly, DNNs that predict the clean class labels can be trained from
noisy data pairs if they are first pretrained from noisy data points.
Class2Simi is computationally efficient because not only this transformation is
on-the-fly in mini-batches, but also it just changes loss computation on top of
model prediction into a pairwise manner. Its effectiveness is verified by
extensive experiments. | [
"cs.LG",
"stat.ML"
] |
Registration is a transformation estimation problem between two point clouds,
which has a unique and critical role in numerous computer vision applications.
The developments of optimization-based methods and deep learning methods have
improved registration robustness and efficiency. Recently, the combinations of
optimization-based and deep learning methods have further improved performance.
However, the connections between optimization-based and deep learning methods
are still unclear. Moreover, with the recent development of 3D sensors and 3D
reconstruction techniques, a new research direction emerges to align
cross-source point clouds. This survey conducts a comprehensive survey,
including both same-source and cross-source registration methods, and summarize
the connections between optimization-based and deep learning methods, to
provide further research insight. This survey also builds a new benchmark to
evaluate the state-of-the-art registration algorithms in solving cross-source
challenges. Besides, this survey summarizes the benchmark data sets and
discusses point cloud registration applications across various domains.
Finally, this survey proposes potential research directions in this rapidly
growing field. | [
"cs.CV"
] |
We study the stochastic multi-armed bandit problem with the graph-based
feedback structure introduced by Mannor and Shamir. We analyze the performance
of the two most prominent stochastic bandit algorithms, Thompson Sampling and
Upper Confidence Bound (UCB), in the graph-based feedback setting. We show that
these algorithms achieve regret guarantees that combine the graph structure and
the gaps between the means of the arm distributions. Surprisingly this holds
despite the fact that these algorithms do not explicitly use the graph
structure to select arms; they observe the additional feedback but do not
explore based on it. Towards this result we introduce a "layering technique"
highlighting the commonalities in the two algorithms. | [
"cs.LG",
"cs.DS",
"stat.ML"
] |
One-shot Neural Architecture Search (NAS) aims to minimize the computational
expense of discovering state-of-the-art models. However, in the past year
attention has been drawn to the comparable performance of naive random search
across the same search spaces used by leading NAS algorithms. To address this,
we explore the effects of drastically relaxing the NAS search space, and we
present Bonsai-Net, an efficient one-shot NAS method to explore our relaxed
search space. Bonsai-Net is built around a modified differential pruner and can
consistently discover state-of-the-art architectures that are significantly
better than random search with fewer parameters than other state-of-the-art
methods. Additionally, Bonsai-Net performs simultaneous model search and
training, dramatically reducing the total time it takes to generate
fully-trained models from scratch. | [
"cs.LG",
"stat.ML"
] |
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