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Deep neural networks are data hungry models and thus face difficulties when
attempting to train on small text datasets. Transfer learning is a potential
solution but their effectiveness in the text domain is not as explored as in
areas such as image analysis. In this paper, we study the problem of transfer
learning for text summarization and discuss why existing state-of-the-art
models fail to generalize well on other (unseen) datasets. We propose a
reinforcement learning framework based on a self-critic policy gradient
approach which achieves good generalization and state-of-the-art results on a
variety of datasets. Through an extensive set of experiments, we also show the
ability of our proposed framework to fine-tune the text summarization model
using only a few training samples. To the best of our knowledge, this is the
first work that studies transfer learning in text summarization and provides a
generic solution that works well on unseen data. | [
"cs.LG",
"cs.CL",
"stat.ML",
"I.2.6; I.2.7; I.2.10"
] |
We consider the problem of building a state representation model in a
continual fashion. As the environment changes, the aim is to efficiently
compress the sensory state's information without losing past knowledge. The
learned features are then fed to a Reinforcement Learning algorithm to learn a
policy. We propose to use Variational Auto-Encoders for state representation,
and Generative Replay, i.e. the use of generated samples, to maintain past
knowledge. We also provide a general and statistically sound method for
automatic environment change detection. Our method provides efficient state
representation as well as forward transfer, and avoids catastrophic forgetting.
The resulting model is capable of incrementally learning information without
using past data and with a bounded system size. | [
"cs.LG",
"stat.ML"
] |
It is our conjecture that the variability of colors in a pathology image
effects the interpretation of pathology cases, whether it is diagnostic
accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital
pathology images are analyzed to quantify the perceived difference in color
that occurs due to display aging, in particular a change in the maximum
luminance, white point, and color gamut. The digital pathology images studied
include diagnostically important features, such as the conspicuity of nuclei.
Three different display aging models are applied to images: aging of luminance
& chrominance, aging of chrominance only, and a stabilized luminance &
chrominance (i.e., no aging). These display models and images are then used to
compare conspicuity of nuclei using CIE deltaE2000, a perceptual color
difference metric. The effect of display aging using these display models and
images is further analyzed through a human reader study designed to quantify
the effects from a clinical perspective. Results from our reader study indicate
significant impact of aged displays on workflow as well as diagnosis as follow.
As compared to the originals (no-aging), slides with the effect of aging
simulated were significantly more difficult to read (p-value of 0.0005) and
took longer to score (p-value of 0.02). Moreover, luminance+chrominance aging
significantly reduced inter-session percent agreement of diagnostic scores
(p-value of 0.0418). | [
"cs.CV",
"cs.GR"
] |
Information-theoretic quantities like entropy and mutual information have
found numerous uses in machine learning. It is well known that there is a
strong connection between these entropic quantities and submodularity since
entropy over a set of random variables is submodular. In this paper, we study
combinatorial information measures that generalize independence, (conditional)
entropy, (conditional) mutual information, and total correlation defined over
sets of (not necessarily random) variables. These measures strictly generalize
the corresponding entropic measures since they are all parameterized via
submodular functions that themselves strictly generalize entropy. Critically,
we show that, unlike entropic mutual information in general, the submodular
mutual information is actually submodular in one argument, holding the other
fixed, for a large class of submodular functions whose third-order partial
derivatives satisfy a non-negativity property. This turns out to include a
number of practically useful cases such as the facility location and set-cover
functions. We study specific instantiations of the submodular information
measures on these, as well as the probabilistic coverage, graph-cut, and
saturated coverage functions, and see that they all have mathematically
intuitive and practically useful expressions. Regarding applications, we
connect the maximization of submodular (conditional) mutual information to
problems such as mutual-information-based, query-based, and privacy-preserving
summarization -- and we connect optimizing the multi-set submodular mutual
information to clustering and robust partitioning. | [
"cs.LG",
"cs.DS",
"cs.IT",
"math.IT",
"math.OC",
"stat.ML"
] |
Synthesizing 3D human motion plays an important role in many graphics
applications as well as understanding human activity. While many efforts have
been made on generating realistic and natural human motion, most approaches
neglect the importance of modeling human-scene interactions and affordance. On
the other hand, affordance reasoning (e.g., standing on the floor or sitting on
the chair) has mainly been studied with static human pose and gestures, and it
has rarely been addressed with human motion. In this paper, we propose to
bridge human motion synthesis and scene affordance reasoning. We present a
hierarchical generative framework to synthesize long-term 3D human motion
conditioning on the 3D scene structure. Building on this framework, we further
enforce multiple geometry constraints between the human mesh and scene point
clouds via optimization to improve realistic synthesis. Our experiments show
significant improvements over previous approaches on generating natural and
physically plausible human motion in a scene. | [
"cs.CV"
] |
Graph Neural Networks (GNNs) are the predominant technique for learning over
graphs. However, there is relatively little understanding of why GNNs are
successful in practice and whether they are necessary for good performance.
Here, we show that for many standard transductive node classification
benchmarks, we can exceed or match the performance of state-of-the-art GNNs by
combining shallow models that ignore the graph structure with two simple
post-processing steps that exploit correlation in the label structure: (i) an
"error correlation" that spreads residual errors in training data to correct
errors in test data and (ii) a "prediction correlation" that smooths the
predictions on the test data. We call this overall procedure Correct and Smooth
(C&S), and the post-processing steps are implemented via simple modifications
to standard label propagation techniques from early graph-based semi-supervised
learning methods. Our approach exceeds or nearly matches the performance of
state-of-the-art GNNs on a wide variety of benchmarks, with just a small
fraction of the parameters and orders of magnitude faster runtime. For
instance, we exceed the best known GNN performance on the OGB-Products dataset
with 137 times fewer parameters and greater than 100 times less training time.
The performance of our methods highlights how directly incorporating label
information into the learning algorithm (as was done in traditional techniques)
yields easy and substantial performance gains. We can also incorporate our
techniques into big GNN models, providing modest gains. Our code for the OGB
results is at https://github.com/Chillee/CorrectAndSmooth. | [
"cs.LG",
"cs.SI"
] |
In this paper, we propose a generative model, Temporal Generative Adversarial
Nets (TGAN), which can learn a semantic representation of unlabeled videos, and
is capable of generating videos. Unlike existing Generative Adversarial Nets
(GAN)-based methods that generate videos with a single generator consisting of
3D deconvolutional layers, our model exploits two different types of
generators: a temporal generator and an image generator. The temporal generator
takes a single latent variable as input and outputs a set of latent variables,
each of which corresponds to an image frame in a video. The image generator
transforms a set of such latent variables into a video. To deal with
instability in training of GAN with such advanced networks, we adopt a recently
proposed model, Wasserstein GAN, and propose a novel method to train it stably
in an end-to-end manner. The experimental results demonstrate the effectiveness
of our methods. | [
"cs.LG",
"cs.CV"
] |
Although impressive results have been achieved for age progression and
regression, there remain two major issues in generative adversarial networks
(GANs)-based methods: 1) conditional GANs (cGANs)-based methods can learn
various effects between any two age groups in a single model, but are
insufficient to characterize some specific patterns due to completely shared
convolutions filters; and 2) GANs-based methods can, by utilizing several
models to learn effects independently, learn some specific patterns, however,
they are cumbersome and require age label in advance. To address these
deficiencies and have the best of both worlds, this paper introduces a
dropout-like method based on GAN~(RoutingGAN) to route different effects in a
high-level semantic feature space. Specifically, we first disentangle the
age-invariant features from the input face, and then gradually add the effects
to the features by residual routers that assign the convolution filters to
different age groups by dropping out the outputs of others. As a result, the
proposed RoutingGAN can simultaneously learn various effects in a single model,
with convolution filters being shared in part to learn some specific effects.
Experimental results on two benchmarked datasets demonstrate superior
performance over existing methods both qualitatively and quantitatively. | [
"cs.CV"
] |
We propose a new benchmark environment for evaluating Reinforcement Learning
(RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation
emulator modified to expose a simple control API that enables rich game-state
representations. We argue that the PlayStation serves as a suitable progression
for agent evaluation and propose a framework for such an evaluation. We build
an action-driven abstraction for a PlayStation game with support for the OpenAI
Gym interface and demonstrate its use by running OpenAI Baselines. | [
"cs.LG",
"cs.AI"
] |
Comorbid diseases co-occur and progress via complex temporal patterns that
vary among individuals. In electronic health records we can observe the
different diseases a patient has, but can only infer the temporal relationship
between each co-morbid condition. Learning such temporal patterns from event
data is crucial for understanding disease pathology and predicting prognoses.
To this end, we develop deep diffusion processes (DDP) to model "dynamic
comorbidity networks", i.e., the temporal relationships between comorbid
disease onsets expressed through a dynamic graph. A DDP comprises events
modelled as a multi-dimensional point process, with an intensity function
parameterized by the edges of a dynamic weighted graph. The graph structure is
modulated by a neural network that maps patient history to edge weights,
enabling rich temporal representations for disease trajectories. The DDP
parameters decouple into clinically meaningful components, which enables
serving the dual purpose of accurate risk prediction and intelligible
representation of disease pathology. We illustrate these features in
experiments using cancer registry data. | [
"cs.LG",
"stat.ML"
] |
Generative Adversarial Networks fostered a newfound interest in generative
models, resulting in a swelling wave of new works that new-coming researchers
may find formidable to surf. In this paper, we intend to help those
researchers, by splitting that incoming wave into six "fronts": Architectural
Contributions, Conditional Techniques, Normalization and Constraint
Contributions, Loss Functions, Image-to-image Translations, and Validation
Metrics. The division in fronts organizes literature into approachable blocks,
ultimately communicating to the reader how the area is evolving. Previous
surveys in the area, which this works also tabulates, focus on a few of those
fronts, leaving a gap that we propose to fill with a more integrated,
comprehensive overview. Here, instead of an exhaustive survey, we opt for a
straightforward review: our target is to be an entry point to this vast
literature, and also to be able to update experienced researchers to the newest
techniques. | [
"cs.CV",
"cs.LG"
] |
Gaussian processes are a versatile framework for learning unknown functions
in a manner that permits one to utilize prior information about their
properties. Although many different Gaussian process models are readily
available when the input space is Euclidean, the choice is much more limited
for Gaussian processes whose input space is an undirected graph. In this work,
we leverage the stochastic partial differential equation characterization of
Mat\'ern Gaussian processes - a widely-used model class in the Euclidean
setting - to study their analog for undirected graphs. We show that the
resulting Gaussian processes inherit various attractive properties of their
Euclidean and Riemannian analogs and provide techniques that allow them to be
trained using standard methods, such as inducing points. This enables graph
Mat\'ern Gaussian processes to be employed in mini-batch and non-conjugate
settings, thereby making them more accessible to practitioners and easier to
deploy within larger learning frameworks. | [
"stat.ML",
"cs.LG"
] |
In this paper, we propose an easily trained yet powerful representation
learning approach with performance highly competitive to deep neural networks
in a digital pathology image segmentation task. The method, called sparse
coding driven deep decision tree ensembles that we abbreviate as ScD2TE,
provides a new perspective on representation learning. We explore the
possibility of stacking several layers based on non-differentiable pairwise
modules and generate a densely concatenated architecture holding the
characteristics of feature map reuse and end-to-end dense learning. Under this
architecture, fast convolutional sparse coding is used to extract multi-level
features from the output of each layer. In this way, rich image appearance
models together with more contextual information are integrated by learning a
series of decision tree ensembles. The appearance and the high-level context
features of all the previous layers are seamlessly combined by concatenating
them to feed-forward as input, which in turn makes the outputs of subsequent
layers more accurate and the whole model efficient to train. Compared with deep
neural networks, our proposed ScD2TE does not require back-propagation
computation and depends on less hyper-parameters. ScD2TE is able to achieve a
fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the
superiority of our segmentation technique by evaluating it on the multi-disease
state and multi-organ dataset where consistently higher performances were
obtained for comparison against several state-of-the-art deep learning methods
such as convolutional neural networks (CNN), fully convolutional networks
(FCN), etc. | [
"cs.CV"
] |
Convolutional Neural Networks (CNNs) have shown impressive performance in
computer vision tasks such as image classification, detection, and
segmentation. Moreover, recent work in Generative Adversarial Networks (GANs)
has highlighted the importance of learning by progressively increasing the
difficulty of a learning task [26]. When learning a network from scratch, the
information propagated within the network during the earlier stages of training
can contain distortion artifacts due to noise which can be detrimental to
training. In this paper, we propose an elegant curriculum based scheme that
smoothes the feature embedding of a CNN using anti-aliasing or low-pass
filters. We propose to augment the train-ing of CNNs by controlling the amount
of high frequency information propagated within the CNNs as training
progresses, by convolving the output of a CNN feature map of each layer with a
Gaussian kernel. By decreasing the variance of the Gaussian kernel, we
gradually increase the amount of high-frequency information available within
the network for inference. As the amount of information in the feature maps
increases during training, the network is able to progressively learn better
representations of the data. Our proposed augmented training scheme
significantly improves the performance of CNNs on various vision tasks without
either adding additional trainable parameters or an auxiliary regularization
objective. The generality of our method is demonstrated through empirical
performance gains in CNN architectures across four different tasks: transfer
learning, cross-task transfer learning, and generative models. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Point clouds are the native output of many real-world 3D sensors. To borrow
the success of 2D convolutional network architectures, a majority of popular 3D
perception models voxelize the points, which can result in a loss of local
geometric details that cannot be recovered. In this paper, we propose a novel
learnable convolution layer for processing 3D point cloud data directly.
Instead of discretizing points into fixed voxels, we deform our learnable 3D
filters to match with the point cloud shape. We propose to combine voxelized
backbone networks with our deformable filter layer at 1) the network input
stream and 2) the output prediction layers to enhance point level reasoning. We
obtain state-of-the-art results on LiDAR semantic segmentation and producing a
significant gain in performance on LiDAR object detection. | [
"cs.CV"
] |
Graph convolutional networks (GCNs) achieved promising performance in
skeleton-based human action recognition by modeling a sequence of skeletons as
a spatio-temporal graph. Most of the recently proposed GCN-based methods
improve the performance by learning the graph structure at each layer of the
network using a spatial attention applied on a predefined graph Adjacency
matrix that is optimized jointly with model's parameters in an end-to-end
manner. In this paper, we analyze the spatial attention used in spatio-temporal
GCN layers and propose a symmetric spatial attention for better reflecting the
symmetric property of the relative positions of the human body joints when
executing actions. We also highlight the connection of spatio-temporal GCN
layers employing additive spatial attention to bilinear layers, and we propose
the spatio-temporal bilinear network (ST-BLN) which does not require the use of
predefined Adjacency matrices and allows for more flexible design of the model.
Experimental results show that the three models lead to effectively the same
performance. Moreover, by exploiting the flexibility provided by the proposed
ST-BLN, one can increase the efficiency of the model. | [
"cs.CV"
] |
Recent years saw a plethora of work on explaining complex intelligent agents.
One example is the development of several algorithms that generate saliency
maps which show how much each pixel attributed to the agents' decision.
However, most evaluations of such saliency maps focus on image classification
tasks. As far as we know, there is no work that thoroughly compares different
saliency maps for Deep Reinforcement Learning agents. This paper compares four
perturbation-based approaches to create saliency maps for Deep Reinforcement
Learning agents trained on four different Atari 2600 games. All four approaches
work by perturbing parts of the input and measuring how much this affects the
agent's output. The approaches are compared using three computational metrics:
dependence on the learned parameters of the agent (sanity checks), faithfulness
to the agent's reasoning (input degradation), and run-time. In particular,
during the sanity checks we find issues with two approaches and propose a
solution to fix one of those issues. | [
"cs.LG",
"cs.AI",
"cs.NE"
] |
Precise boundary annotations of image regions can be crucial for downstream
applications which rely on region-class semantics. Some document collections
contain densely laid out, highly irregular and overlapping multi-class region
instances with large range in aspect ratio. Fully automatic boundary estimation
approaches tend to be data intensive, cannot handle variable-sized images and
produce sub-optimal results for aforementioned images. To address these issues,
we propose BoundaryNet, a novel resizing-free approach for high-precision
semi-automatic layout annotation. The variable-sized user selected region of
interest is first processed by an attention-guided skip network. The network
optimization is guided via Fast Marching distance maps to obtain a good quality
initial boundary estimate and an associated feature representation. These
outputs are processed by a Residual Graph Convolution Network optimized using
Hausdorff loss to obtain the final region boundary. Results on a challenging
image manuscript dataset demonstrate that BoundaryNet outperforms strong
baselines and produces high-quality semantic region boundaries. Qualitatively,
our approach generalizes across multiple document image datasets containing
different script systems and layouts, all without additional fine-tuning. We
integrate BoundaryNet into a document annotation system and show that it
provides high annotation throughput compared to manual and fully automatic
alternatives. | [
"cs.CV",
"cs.CL",
"cs.MM"
] |
Labelled networks form a very common and important class of data, naturally
appearing in numerous applications in science and engineering. A typical
inference goal is to determine how the vertex labels(or {\em features}) affect
the network's graph structure. A standard approach has been to partition the
network into blocks grouped by distinct values of the feature of interest. A
block-based random graph model -- typically a variant of the stochastic block
model -- is then used to test for evidence of asymmetric behaviour within these
feature-based communities. Nevertheless, the resulting communities often do not
produce a natural partition of the graph. In this work, we introduce a new
generative model, the feature-first block model (FFBM), which is more effective
at describing vertex-labelled undirected graphs and also facilitates the use of
richer queries on labelled networks. We develop a Bayesian framework for
inference with this model, and we present a method to efficiently sample from
the posterior distribution of the FFBM parameters. The FFBM's structure is kept
deliberately simple to retain easy interpretability of the parameter values. We
apply the proposed methods to a variety of network data to extract the most
important features along which the vertices are partitioned. The main
advantages of the proposed approach are that the whole feature-space is used
automatically, and features can be rank-ordered implicitly according to impact.
Any features that do not significantly impact the high-level structure can be
discarded to reduce the problem dimension. In cases where the vertex features
available do not readily explain the community structure in the resulting
network, the approach detects this and is protected against over-fitting.
Results on several real-world datasets illustrate the performance of the
proposed methods. | [
"cs.LG",
"cs.SI",
"stat.AP"
] |
LiDAR-based 3D object detection pushes forward an immense influence on
autonomous vehicles. Due to the limitation of the intrinsic properties of
LiDAR, fewer points are collected at the objects farther away from the sensor.
This imbalanced density of point clouds degrades the detection accuracy but is
generally neglected by previous works. To address the challenge, we propose a
novel two-stage 3D object detection framework, named SIENet. Specifically, we
design the Spatial Information Enhancement (SIE) module to predict the spatial
shapes of the foreground points within proposals, and extract the structure
information to learn the representative features for further box refinement.
The predicted spatial shapes are complete and dense point sets, thus the
extracted structure information contains more semantic representation. Besides,
we design the Hybrid-Paradigm Region Proposal Network (HP-RPN) which includes
multiple branches to learn discriminate features and generate accurate
proposals for the SIE module. Extensive experiments on the KITTI 3D object
detection benchmark show that our elaborately designed SIENet outperforms the
state-of-the-art methods by a large margin. | [
"cs.CV"
] |
Two-dimensional nanomaterials, such as graphene, have been extensively
studied because of their outstanding physical properties. Structure and
geometry optimization of nanopores on such materials is beneficial for their
performances in real-world engineering applications, like water desalination.
However, the optimization process often involves very large number of
experiments or simulations which are expensive and time-consuming. In this
work, we propose a graphene nanopore optimization framework via the combination
of deep reinforcement learning (DRL) and convolutional neural network (CNN) for
efficient water desalination. The DRL agent controls the growth of nanopore by
determining the atom to be removed at each timestep, while the CNN predicts the
performance of nanoporus graphene for water desalination: the water flux and
ion rejection at a certain external pressure. With the synchronous feedback
from CNN-accelerated desalination performance prediction, our DRL agent can
optimize the nanoporous graphene efficiently in an online manner. Molecular
dynamics (MD) simulations on promising DRL-designed graphene nanopores show
that they have higher water flux while maintaining rival ion rejection rate
compared to the normal circular nanopores. Semi-oval shape with rough edges
geometry of DRL-designed pores is found to be the key factor for their high
water desalination performance. Ultimately, this study shows that DRL can be a
powerful tool for material design. | [
"cs.LG",
"physics.chem-ph"
] |
We present a robust method to correct for motion and deformations for
in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI
requires robust alignment across time in the presence of substantial and
unpredictable motion. We make a Markov assumption on the nature of deformations
to take advantage of the temporal structure in the image data. Forward message
passing in the corresponding hidden Markov model (HMM) yields an estimation
algorithm that only has to account for relatively small motion between
consecutive frames. We demonstrate the utility of the temporal model by showing
that its use improves the accuracy of the segmentation propagation through
temporal registration. Our results suggest that the proposed model captures
accurately the temporal dynamics of deformations in in-utero MRI time series. | [
"cs.CV"
] |
Making predictions of future frames is a critical challenge in autonomous
driving research. Most of the existing methods for video prediction attempt to
generate future frames in simple and fixed scenes. In this paper, we propose a
novel and effective optical flow conditioned method for the task of video
prediction with an application to complex urban scenes. In contrast with
previous work, the prediction model only requires video sequences and optical
flow sequences for training and testing. Our method uses the rich
spatial-temporal features in video sequences. The method takes advantage of the
motion information extracting from optical flow maps between neighbor images as
well as previous images. Empirical evaluations on the KITTI dataset and the
Cityscapes dataset demonstrate the effectiveness of our method. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Convolutional neural networks (CNNs) have developed to become powerful models
for various computer vision tasks ranging from object detection to semantic
segmentation. However, most of the state-of-the-art CNNs cannot be deployed
directly on edge devices such as smartphones and drones, which need low latency
under limited power and memory bandwidth. One popular, straightforward approach
to compressing CNNs is network slimming, which imposes $\ell_1$ regularization
on the channel-associated scaling factors via the batch normalization layers
during training. Network slimming thereby identifies insignificant channels
that can be pruned for inference. In this paper, we propose replacing the
$\ell_1$ penalty with an alternative nonconvex, sparsity-inducing penalty in
order to yield a more compressed and/or accurate CNN architecture. We
investigate $\ell_p (0 < p < 1)$, transformed $\ell_1$ (T$\ell_1$), minimax
concave penalty (MCP), and smoothly clipped absolute deviation (SCAD) due to
their recent successes and popularity in solving sparse optimization problems,
such as compressed sensing and variable selection. We demonstrate the
effectiveness of network slimming with nonconvex penalties on three neural
network architectures -- VGG-19, DenseNet-40, and ResNet-164 -- on standard
image classification datasets. Based on the numerical experiments, T$\ell_1$
preserves model accuracy against channel pruning, $\ell_{1/2, 3/4}$ yield
better compressed models with similar accuracies after retraining as $\ell_1$,
and MCP and SCAD provide more accurate models after retraining with similar
compression as $\ell_1$. Network slimming with T$\ell_1$ regularization also
outperforms the latest Bayesian modification of network slimming in compressing
a CNN architecture in terms of memory storage while preserving its model
accuracy after channel pruning. | [
"cs.CV"
] |
In this paper, we describe a reproduction of the Relational Graph
Convolutional Network (RGCN). Using our reproduction, we explain the intuition
behind the model. Our reproduction results empirically validate the correctness
of our implementations using benchmark Knowledge Graph datasets on node
classification and link prediction tasks. Our explanation provides a friendly
understanding of the different components of the RGCN for both users and
researchers extending the RGCN approach. Furthermore, we introduce two new
configurations of the RGCN that are more parameter efficient. The code and
datasets are available at https://github.com/thiviyanT/torch-rgcn. | [
"cs.LG"
] |
Despite learning based methods showing promising results in single view depth
estimation and visual odometry, most existing approaches treat the tasks in a
supervised manner. Recent approaches to single view depth estimation explore
the possibility of learning without full supervision via minimizing photometric
error. In this paper, we explore the use of stereo sequences for learning depth
and visual odometry. The use of stereo sequences enables the use of both
spatial (between left-right pairs) and temporal (forward backward) photometric
warp error, and constrains the scene depth and camera motion to be in a common,
real-world scale. At test time our framework is able to estimate single view
depth and two-view odometry from a monocular sequence. We also show how we can
improve on a standard photometric warp loss by considering a warp of deep
features. We show through extensive experiments that: (i) jointly training for
single view depth and visual odometry improves depth prediction because of the
additional constraint imposed on depths and achieves competitive results for
visual odometry; (ii) deep feature-based warping loss improves upon simple
photometric warp loss for both single view depth estimation and visual
odometry. Our method outperforms existing learning based methods on the KITTI
driving dataset in both tasks. The source code is available at
https://github.com/Huangying-Zhan/Depth-VO-Feat | [
"cs.CV"
] |
Color image segmentation is a crucial step in many computer vision and
pattern recognition applications. This article introduces an adaptive and
unsupervised clustering approach based on Voronoi regions, which can be applied
to solve the color image segmentation problem. The proposed method performs
region splitting and merging within Voronoi regions of the Dirichlet
Tessellated image (also called a Voronoi diagram) , which improves the
efficiency and the accuracy of the number of clusters and cluster centroids
estimation process. Furthermore, the proposed method uses cluster centroid
proximity to merge proximal clusters in order to find the final number of
clusters and cluster centroids. In contrast to the existing adaptive
unsupervised cluster-based image segmentation algorithms, the proposed method
uses K-means clustering algorithm in place of the Fuzzy C-means algorithm to
find the final segmented image. The proposed method was evaluated on three
different unsupervised image segmentation evaluation benchmarks and its results
were compared with two other adaptive unsupervised cluster-based image
segmentation algorithms. The experimental results reported in this article
confirm that the proposed method outperforms the existing algorithms in terms
of the quality of image segmentation results. Also, the proposed method results
in the lowest average execution time per image compared to the existing methods
reported in this article. | [
"cs.CV",
"05B45, 62H30, 54E05, 68T10"
] |
We consider the problem of estimating a particular type of linear
non-Gaussian model. Without resorting to the overcomplete Independent Component
Analysis (ICA), we show that under some mild assumptions, the model is uniquely
identified by a hybrid method. Our method leverages the advantages of
constraint-based methods and independent noise-based methods to handle both
confounded and unconfounded situations. The first step of our method uses the
FCI procedure, which allows confounders and is able to produce asymptotically
correct results. The results, unfortunately, usually determine very few
unconfounded direct causal relations, because whenever it is possible to have a
confounder, it will indicate it. The second step of our procedure finds the
unconfounded causal edges between observed variables among only those adjacent
pairs informed by the FCI results. By making use of the so-called Triad
condition, the third step is able to find confounders and their causal
relations with other variables. Afterward, we apply ICA on a notably smaller
set of graphs to identify remaining causal relationships if needed. Extensive
experiments on simulated data and real-world data validate the correctness and
effectiveness of the proposed method. | [
"cs.LG",
"stat.ML"
] |
This paper introduces the task of few-shot common action localization in time
and space. Given a few trimmed support videos containing the same but unknown
action, we strive for spatio-temporal localization of that action in a long
untrimmed query video. We do not require any class labels, interval bounds, or
bounding boxes. To address this challenging task, we introduce a novel few-shot
transformer architecture with a dedicated encoder-decoder structure optimized
for joint commonality learning and localization prediction, without the need
for proposals. Experiments on our reorganizations of the AVA and UCF101-24
datasets show the effectiveness of our approach for few-shot common action
localization, even when the support videos are noisy. Although we are not
specifically designed for common localization in time only, we also compare
favorably against the few-shot and one-shot state-of-the-art in this setting.
Lastly, we demonstrate that the few-shot transformer is easily extended to
common action localization per pixel. | [
"cs.CV"
] |
Unsupervised Learning based monocular visual odometry (VO) has lately drawn
significant attention for its potential in label-free leaning ability and
robustness to camera parameters and environmental variations. However,
partially due to the lack of drift correction technique, these methods are
still by far less accurate than geometric approaches for large-scale odometry
estimation. In this paper, we propose to leverage graph optimization and loop
closure detection to overcome limitations of unsupervised learning based
monocular visual odometry. To this end, we propose a hybrid VO system which
combines an unsupervised monocular VO called NeuralBundler with a pose graph
optimization back-end. NeuralBundler is a neural network architecture that uses
temporal and spatial photometric loss as main supervision and generates a
windowed pose graph consists of multi-view 6DoF constraints. We propose a novel
pose cycle consistency loss to relieve the tensions in the windowed pose graph,
leading to improved performance and robustness. In the back-end, a global pose
graph is built from local and loop 6DoF constraints estimated by NeuralBundler
and is optimized over SE(3). Empirical evaluation on the KITTI odometry dataset
demonstrates that 1) NeuralBundler achieves state-of-the-art performance on
unsupervised monocular VO estimation, and 2) our whole approach can achieve
efficient loop closing and show favorable overall translational accuracy
compared to established monocular SLAM systems. | [
"cs.CV"
] |
Remote sensing and automatic earth monitoring are key to solve global-scale
challenges such as disaster prevention, land use monitoring, or tackling
climate change. Although there exist vast amounts of remote sensing data, most
of it remains unlabeled and thus inaccessible for supervised learning
algorithms. Transfer learning approaches can reduce the data requirements of
deep learning algorithms. However, most of these methods are pre-trained on
ImageNet and their generalization to remote sensing imagery is not guaranteed
due to the domain gap. In this work, we propose Seasonal Contrast (SeCo), an
effective pipeline to leverage unlabeled data for in-domain pre-training of
remote sensing representations. The SeCo pipeline is composed of two parts.
First, a principled procedure to gather large-scale, unlabeled and uncurated
remote sensing datasets containing images from multiple Earth locations at
different timestamps. Second, a self-supervised algorithm that takes advantage
of time and position invariance to learn transferable representations for
remote sensing applications. We empirically show that models trained with SeCo
achieve better performance than their ImageNet pre-trained counterparts and
state-of-the-art self-supervised learning methods on multiple downstream tasks.
The datasets and models in SeCo will be made public to facilitate transfer
learning and enable rapid progress in remote sensing applications. | [
"cs.CV"
] |
In recent years, deep learning techniques (e.g., U-Net, DeepLab) have
achieved tremendous success in image segmentation. The performance of these
models heavily relies on high-quality ground truth segment labels.
Unfortunately, in many real-world problems, ground truth segment labels often
have geometric annotation errors due to manual annotation mistakes, GPS errors,
or visually interpreting background imagery at a coarse resolution. Such
location errors will significantly impact the training performance of existing
deep learning algorithms. Existing research on label errors either models
ground truth errors in label semantics (assuming label locations to be correct)
or models label location errors with simple square patch shifting. These
methods cannot fully incorporate the geometric properties of label location
errors. To fill the gap, this paper proposes a generic learning framework based
on the EM algorithm to update deep learning model parameters and infer hidden
true label locations simultaneously. Evaluations on a real-world hydrological
dataset in the streamline refinement application show that the proposed
framework outperforms baseline methods in classification accuracy (reducing the
number of false positives by 67% and reducing the number of false negatives by
55%). | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Standard segmentation of medical images based on full-supervised
convolutional networks demands accurate dense annotations. Such learning
framework is built on laborious manual annotation with restrict demands for
expertise, leading to insufficient high-quality labels. To overcome such
limitation and exploit massive weakly labeled data, we relaxed the rigid
labeling requirement and developed a semi-supervised learning framework based
on a teacher-student fashion for organ and lesion segmentation with partial
dense-labeled supervision and supplementary loose bounding-box supervision
which are easier to acquire. Observing the geometrical relation of an organ and
its inner lesions in most cases, we propose a hierarchical organ-to-lesion
(O2L) attention module in a teacher segmentor to produce pseudo-labels. Then a
student segmentor is trained with combinations of manual-labeled and
pseudo-labeled annotations. We further proposed a localization branch realized
via an aggregation of high-level features in a deep decoder to predict
locations of organ and lesion, which enriches student segmentor with precise
localization information. We validated each design in our model on LiTS
challenge datasets by ablation study and showed its state-of-the-art
performance compared with recent methods. We show our model is robust to the
quality of bounding box and achieves comparable performance compared with
full-supervised learning methods. | [
"cs.CV"
] |
We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal
Point Cloud Representations of dynamically moving or evolving objects. Our goal
is to enable information aggregation over time and the interrogation of object
state at any spatiotemporal neighborhood in the past, observed or not.
Different from previous work, CaSPR learns representations that support
spacetime continuity, are robust to variable and irregularly spacetime-sampled
point clouds, and generalize to unseen object instances. Our approach divides
the problem into two subtasks. First, we explicitly encode time by mapping an
input point cloud sequence to a spatiotemporally-canonicalized object space. We
then leverage this canonicalization to learn a spatiotemporal latent
representation using neural ordinary differential equations and a generative
model of dynamically evolving shapes using continuous normalizing flows. We
demonstrate the effectiveness of our method on several applications including
shape reconstruction, camera pose estimation, continuous spatiotemporal
sequence reconstruction, and correspondence estimation from irregularly or
intermittently sampled observations. | [
"cs.CV",
"cs.LG"
] |
The significant progress on Generative Adversarial Networks (GANs) have made
it possible to generate surprisingly realistic images for single object based
on natural language descriptions. However, controlled generation of images for
multiple entities with explicit interactions is still difficult to achieve due
to the scene layout generation heavily suffer from the diversity object scaling
and spatial locations. In this paper, we proposed a novel framework for
generating realistic image layout from textual scene graphs. In our framework,
a spatial constraint module is designed to fit reasonable scaling and spatial
layout of object pairs with considering relationship between them. Moreover, a
contextual fusion module is introduced for fusing pair-wise spatial information
in terms of object dependency in scene graph. By using these two modules, our
proposed framework tends to generate more commonsense layout which is helpful
for realistic image generation. Experimental results including quantitative
results, qualitative results and user studies on two different scene graph
datasets demonstrate our proposed framework's ability to generate complex and
logical layout with multiple objects from scene graph. | [
"cs.CV"
] |
Image generation has rapidly evolved in recent years. Modern architectures
for adversarial training allow to generate even high resolution images with
remarkable quality. At the same time, more and more effort is dedicated towards
controlling the content of generated images. In this paper, we take one further
step in this direction and propose a conditional generative adversarial network
(GAN) that generates images with a defined number of objects from given
classes. This entails two fundamental abilities (1) being able to generate
high-quality images given a complex constraint and (2) being able to count
object instances per class in a given image. Our proposed model modularly
extends the successful StyleGAN2 architecture with a count-based conditioning
as well as with a regression sub-network to count the number of generated
objects per class during training. In experiments on three different datasets,
we show that the proposed model learns to generate images according to the
given multiple-class count condition even in the presence of complex
backgrounds. In particular, we propose a new dataset, CityCount, which is
derived from the Cityscapes street scenes dataset, to evaluate our approach in
a challenging and practically relevant scenario. | [
"cs.CV",
"cs.LG"
] |
The capability to perform facial analysis from video sequences has
significant potential to positively impact in many areas of life. One such area
relates to the medical domain to specifically aid in the diagnosis and
rehabilitation of patients with facial palsy. With this application in mind,
this paper presents an end-to-end framework, named 3DPalsyNet, for the tasks of
mouth motion recognition and facial palsy grading. 3DPalsyNet utilizes a 3D CNN
architecture with a ResNet backbone for the prediction of these dynamic tasks.
Leveraging transfer learning from a 3D CNNs pre-trained on the Kinetics data
set for general action recognition, the model is modified to apply joint
supervised learning using center and softmax loss concepts. 3DPalsyNet is
evaluated on a test set consisting of individuals with varying ranges of facial
palsy and mouth motions and the results have shown an attractive level of
classification accuracy in these task of 82% and 86% respectively. The frame
duration and the loss function affect was studied in terms of the predictive
qualities of the proposed 3DPalsyNet, where it was found shorter frame
duration's of 8 performed best for this specific task. Centre loss and softmax
have shown improvements in spatio-temporal feature learning than softmax loss
alone, this is in agreement with earlier work involving the spatial domain. | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG",
"cs.NE"
] |
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field. | [
"cs.LG",
"eess.IV",
"stat.ML"
] |
The tracking algorithm performance depends on video content. This paper
presents a new multi-object tracking approach which is able to cope with video
content variations. First the object detection is improved using Kanade-
Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an
appropriate tracker is selected among a KLT-based tracker and a discriminative
appearance-based tracker. This selection is supported by an online tracking
evaluation. The approach has been experimented on three public video datasets.
The experimental results show a better performance of the proposed approach
compared to recent state of the art trackers. | [
"cs.CV"
] |
In this paper, we investigate the impacts of three main aspects of visual
tracking, i.e., the backbone network, the attentional mechanism, and the
detection component, and propose a Siamese Attentional Keypoint Network, dubbed
SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese
lightweight hourglass network is specially designed for visual tracking. It
takes advantage of the benefits of the repeated bottom-up and top-down
inference to capture more global and local contextual information at multiple
scales. Secondly, a novel cross-attentional module is utilized to leverage both
channel-wise and spatial intermediate attentional information, which can
enhance both discriminative and localization capabilities of feature maps.
Thirdly, a keypoints detection approach is invented to trace any target object
by detecting the top-left corner point, the centroid point, and the
bottom-right corner point of its bounding box. Therefore, our SATIN tracker not
only has a strong capability to learn more effective object representations,
but also is computational and memory storage efficiency, either during the
training or testing stages. To the best of our knowledge, we are the first to
propose this approach. Without bells and whistles, experimental results
demonstrate that our approach achieves state-of-the-art performance on several
recent benchmark datasets, at a speed far exceeding 27 frames per second. | [
"cs.CV",
"cs.AI",
"cs.MM"
] |
Convolutional Neural Network (CNN) based image segmentation has made great
progress in recent years. However, video object segmentation remains a
challenging task due to its high computational complexity. Most of the previous
methods employ a two-stream CNN framework to handle spatial and motion features
separately. In this paper, we propose an end-to-end encoder-decoder style 3D
CNN to aggregate spatial and temporal information simultaneously for video
object segmentation. To efficiently process video, we propose 3D separable
convolution for the pyramid pooling module and decoder, which dramatically
reduces the number of operations while maintaining the performance. Moreover,
we also extend our framework to video action segmentation by adding an extra
classifier to predict the action label for actors in videos. Extensive
experiments on several video datasets demonstrate the superior performance of
the proposed approach for action and object segmentation compared to the
state-of-the-art. | [
"cs.CV",
"eess.IV"
] |
Recent contributions have demonstrated that it is possible to recognize the
pose of humans densely and accurately given a large dataset of poses annotated
in detail. In principle, the same approach could be extended to any animal
class, but the effort required for collecting new annotations for each case
makes this strategy impractical, despite important applications in natural
conservation, science and business. We show that, at least for proximal animal
classes such as chimpanzees, it is possible to transfer the knowledge existing
in dense pose recognition for humans, as well as in more general object
detectors and segmenters, to the problem of dense pose recognition in other
classes. We do this by (1) establishing a DensePose model for the new animal
which is also geometrically aligned to humans (2) introducing a multi-head
R-CNN architecture that facilitates transfer of multiple recognition tasks
between classes, (3) finding which combination of known classes can be
transferred most effectively to the new animal and (4) using self-calibrated
uncertainty heads to generate pseudo-labels graded by quality for training a
model for this class. We also introduce two benchmark datasets labelled in the
manner of DensePose for the class chimpanzee and use them to evaluate our
approach, showing excellent transfer learning performance. | [
"cs.CV"
] |
Despite the rapid growth of online shopping and research interest in the
relationship between online and in-store shopping, national-level modeling and
investigation of the demand for online shopping with a prediction focus remain
limited in the literature. This paper differs from prior work and leverages two
recent releases of the U.S. National Household Travel Survey (NHTS) data for
2009 and 2017 to develop machine learning (ML) models, specifically gradient
boosting machine (GBM), for predicting household-level online shopping
purchases. The NHTS data allow for not only conducting nationwide investigation
but also at the level of households, which is more appropriate than at the
individual level given the connected consumption and shopping needs of members
in a household. We follow a systematic procedure for model development
including employing Recursive Feature Elimination algorithm to select input
variables (features) in order to reduce the risk of model overfitting and
increase model explainability. Extensive post-modeling investigation is
conducted in a comparative manner between 2009 and 2017, including quantifying
the importance of each input variable in predicting online shopping demand, and
characterizing value-dependent relationships between demand and the input
variables. In doing so, two latest advances in machine learning techniques,
namely Shapley value-based feature importance and Accumulated Local Effects
plots, are adopted to overcome inherent drawbacks of the popular techniques in
current ML modeling. The modeling and investigation are performed both at the
national level and for three of the largest cities (New York, Los Angeles, and
Houston). The models developed and insights gained can be used for online
shopping-related freight demand generation and may also be considered for
evaluating the potential impact of relevant policies on online shopping demand. | [
"cs.LG"
] |
Most existing object instance detection and segmentation models only work
well on fairly balanced benchmarks where per-category training sample numbers
are comparable, such as COCO. They tend to suffer performance drop on realistic
datasets that are usually long-tailed. This work aims to study and address such
open challenges. Specifically, we systematically investigate performance drop
of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the
recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate
classification of object proposals. Based on such an observation, we first
consider various techniques for improving long-tail classification performance
which indeed enhance instance segmentation results. We then propose a simple
calibration framework to more effectively alleviate classification head bias
with a bi-level class balanced sampling approach. Without bells and whistles,
it significantly boosts the performance of instance segmentation for tail
classes on the recent LVIS dataset and our sampled COCO-LT dataset. Our
analysis provides useful insights for solving long-tail instance detection and
segmentation problems, and the straightforward \emph{SimCal} method can serve
as a simple but strong baseline. With the method we have won the 2019 LVIS
challenge. Codes and models are available at https://github.com/twangnh/SimCal. | [
"cs.CV"
] |
Bird's-eye-view (BEV) is a powerful and widely adopted representation for
road scenes that captures surrounding objects and their spatial locations,
along with overall context in the scene. In this work, we focus on bird's eye
semantic segmentation, a task that predicts pixel-wise semantic segmentation in
BEV from side RGB images. This task is made possible by simulators such as
Carla, which allow for cheap data collection, arbitrary camera placements, and
supervision in ways otherwise not possible in the real world. There are two
main challenges to this task: the view transformation from side view to bird's
eye view, as well as transfer learning to unseen domains. Existing work
transforms between views through fully connected layers and transfer learns via
GANs. This suffers from a lack of depth reasoning and performance degradation
across domains. Our novel 2-staged perception pipeline explicitly predicts
pixel depths and combines them with pixel semantics in an efficient manner,
allowing the model to leverage depth information to infer objects' spatial
locations in the BEV. In addition, we transfer learning by abstracting
high-level geometric features and predicting an intermediate representation
that is common across different domains. We publish a new dataset called
BEVSEG-Carla and show that our approach improves state-of-the-art by 24% mIoU
and performs well when transferred to a new domain. | [
"cs.CV"
] |
Recent theoretical work studies sample-efficient reinforcement learning (RL)
extensively in two settings: learning interactively in the environment (online
RL), or learning from an offline dataset (offline RL). However, existing
algorithms and theories for learning near-optimal policies in these two
settings are rather different and disconnected. Towards bridging this gap, this
paper initiates the theoretical study of policy finetuning, that is, online RL
where the learner has additional access to a "reference policy" $\mu$ close to
the optimal policy $\pi_\star$ in a certain sense. We consider the policy
finetuning problem in episodic Markov Decision Processes (MDPs) with $S$
states, $A$ actions, and horizon length $H$. We first design a sharp offline
reduction algorithm -- which simply executes $\mu$ and runs offline policy
optimization on the collected dataset -- that finds an $\varepsilon$
near-optimal policy within $\widetilde{O}(H^3SC^\star/\varepsilon^2)$ episodes,
where $C^\star$ is the single-policy concentrability coefficient between $\mu$
and $\pi_\star$. This offline result is the first that matches the sample
complexity lower bound in this setting, and resolves a recent open question in
offline RL. We then establish an $\Omega(H^3S\min\{C^\star, A\}/\varepsilon^2)$
sample complexity lower bound for any policy finetuning algorithm, including
those that can adaptively explore the environment. This implies that -- perhaps
surprisingly -- the optimal policy finetuning algorithm is either offline
reduction or a purely online RL algorithm that does not use $\mu$. Finally, we
design a new hybrid offline/online algorithm for policy finetuning that
achieves better sample complexity than both vanilla offline reduction and
purely online RL algorithms, in a relaxed setting where $\mu$ only satisfies
concentrability partially up to a certain time step. | [
"cs.LG",
"stat.ML"
] |
The restricted Boltzmann machine (RBM) is a representative generative model
based on the concept of statistical mechanics. In spite of the strong merit of
interpretability, unavailability of backpropagation makes it less competitive
than other generative models. Here we derive differentiable loss functions for
both binary and multinary RBMs. Then we demonstrate their learnability and
performance by generating colored face images. | [
"cs.CV",
"cs.LG"
] |
Distributed computing is a standard way to scale up machine learning and data
science algorithms to process large amounts of data. In such settings, avoiding
communication amongst machines is paramount for achieving high performance.
Rather than distribute the computation of existing algorithms, a common
practice for avoiding communication is to compute local solutions or parameter
estimates on each machine and then combine the results; in many convex
optimization problems, even simple averaging of local solutions can work well.
However, these schemes do not work when the local solutions are not unique.
Spectral methods are a collection of such problems, where solutions are
orthonormal bases of the leading invariant subspace of an associated data
matrix, which are only unique up to rotation and reflections. Here, we develop
a communication-efficient distributed algorithm for computing the leading
invariant subspace of a data matrix. Our algorithm uses a novel alignment
scheme that minimizes the Procrustean distance between local solutions and a
reference solution, and only requires a single round of communication. For the
important case of principal component analysis (PCA), we show that our
algorithm achieves a similar error rate to that of a centralized estimator. We
present numerical experiments demonstrating the efficacy of our proposed
algorithm for distributed PCA, as well as other problems where solutions
exhibit rotational symmetry, such as node embeddings for graph data and
spectral initialization for quadratic sensing. | [
"stat.ML",
"cs.DC",
"cs.LG",
"cs.NA",
"math.NA"
] |
Transfer learning, which allows a source task to affect the inductive bias of
the target task, is widely used in computer vision. The typical way of
conducting transfer learning with deep neural networks is to fine-tune a model
pre-trained on the source task using data from the target task. In this paper,
we propose an adaptive fine-tuning approach, called SpotTune, which finds the
optimal fine-tuning strategy per instance for the target data. In SpotTune,
given an image from the target task, a policy network is used to make routing
decisions on whether to pass the image through the fine-tuned layers or the
pre-trained layers. We conduct extensive experiments to demonstrate the
effectiveness of the proposed approach. Our method outperforms the traditional
fine-tuning approach on 12 out of 14 standard datasets.We also compare SpotTune
with other state-of-the-art fine-tuning strategies, showing superior
performance. On the Visual Decathlon datasets, our method achieves the highest
score across the board without bells and whistles. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Graph Neural Networks (GNNs) have boosted the performance for many
graph-related tasks. Despite the great success, recent studies have shown that
GNNs are highly vulnerable to adversarial attacks, where adversaries can
mislead the GNNs' prediction by modifying graphs. On the other hand, the
explanation of GNNs (GNNExplainer) provides a better understanding of a trained
GNN model by generating a small subgraph and features that are most influential
for its prediction. In this paper, we first perform empirical studies to
validate that GNNExplainer can act as an inspection tool and have the potential
to detect the adversarial perturbations for graphs. This finding motivates us
to further initiate a new problem investigation: Whether a graph neural network
and its explanations can be jointly attacked by modifying graphs with malicious
desires? It is challenging to answer this question since the goals of
adversarial attacks and bypassing the GNNExplainer essentially contradict each
other. In this work, we give a confirmative answer to this question by
proposing a novel attack framework (GEAttack), which can attack both a GNN
model and its explanations by simultaneously exploiting their vulnerabilities.
Extensive experiments on two explainers (GNNExplainer and PGExplainer) under
various real-world datasets demonstrate the effectiveness of the proposed
method. | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.SI"
] |
Although there exist several libraries for deep learning on graphs, they are
aiming at implementing basic operations for graph deep learning. In the
research community, implementing and benchmarking various advanced tasks are
still painful and time-consuming with existing libraries. To facilitate graph
deep learning research, we introduce DIG: Dive into Graphs, a research-oriented
library that integrates unified and extensible implementations of common graph
deep learning algorithms for several advanced tasks. Currently, we consider
graph generation, self-supervised learning on graphs, explainability of graph
neural networks, and deep learning on 3D graphs. For each direction, we provide
unified implementations of data interfaces, common algorithms, and evaluation
metrics. Altogether, DIG is an extensible, open-source, and turnkey library for
researchers to develop new methods and effortlessly compare with common
baselines using widely used datasets and evaluation metrics. Source code is
available at https://github.com/divelab/DIG. | [
"cs.LG"
] |
The quality of datasets is one of the key factors that affect the accuracy of
aerodynamic data models. For example, in the uniformly sampled Burgers'
dataset, the insufficient high-speed data is overwhelmed by massive low-speed
data. Predicting high-speed data is more difficult than predicting low-speed
data, owing to that the number of high-speed data is limited, i.e. the quality
of the Burgers' dataset is not satisfactory. To improve the quality of
datasets, traditional methods usually employ the data resampling technology to
produce enough data for the insufficient parts in the original datasets before
modeling, which increases computational costs. Recently, the mixtures of
experts have been used in natural language processing to deal with different
parts of sentences, which provides a solution for eliminating the need for data
resampling in aerodynamic data modeling. Motivated by this, we propose the
multi-task learning (MTL), a datasets quality-adaptive learning scheme, which
combines task allocation and aerodynamic characteristics learning together to
disperse the pressure of the entire learning task. The task allocation divides
a whole learning task into several independent subtasks, while the aerodynamic
characteristics learning learns these subtasks simultaneously to achieve better
precision. Two experiments with poor quality datasets are conducted to verify
the data quality-adaptivity of the MTL to datasets. The results show than the
MTL is more accurate than FCNs and GANs in poor quality datasets. | [
"cs.LG",
"cs.AI"
] |
Hyperbolic space is a geometry that is known to be well-suited for
representation learning of data with an underlying hierarchical structure. In
this paper, we present a novel hyperbolic distribution called
\textit{pseudo-hyperbolic Gaussian}, a Gaussian-like distribution on hyperbolic
space whose density can be evaluated analytically and differentiated with
respect to the parameters. Our distribution enables the gradient-based learning
of the probabilistic models on hyperbolic space that could never have been
considered before. Also, we can sample from this hyperbolic probability
distribution without resorting to auxiliary means like rejection sampling. As
applications of our distribution, we develop a hyperbolic-analog of variational
autoencoder and a method of probabilistic word embedding on hyperbolic space.
We demonstrate the efficacy of our distribution on various datasets including
MNIST, Atari 2600 Breakout, and WordNet. | [
"stat.ML",
"cs.LG"
] |
Training (source) domain bias affects state-of-the-art object detectors, such
as Faster R-CNN, when applied to new (target) domains. To alleviate this
problem, researchers proposed various domain adaptation methods to improve
object detection results in the cross-domain setting, e.g. by translating
images with ground-truth labels from the source domain to the target domain
using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced
learning in a smart and efficient way, in this paper, we propose a novel
self-paced algorithm that learns from easy to hard. Our method is simple and
effective, without any overhead during inference. It uses only pseudo-labels
for samples taken from the target domain, i.e. the domain adaptation is
unsupervised. We conduct experiments on four cross-domain benchmarks, showing
better results than the state of the art. We also perform an ablation study
demonstrating the utility of each component in our framework. Additionally, we
study the applicability of our framework to other object detectors.
Furthermore, we compare our difficulty measure with other measures from the
related literature, proving that it yields superior results and that it
correlates well with the performance metric. | [
"cs.CV",
"cs.LG"
] |
Artificial Neural Networks are connectionist systems that perform a given
task by learning on examples without having prior knowledge about the task.
This is done by finding an optimal point estimate for the weights in every
node. Generally, the network using point estimates as weights perform well with
large datasets, but they fail to express uncertainty in regions with little or
no data, leading to overconfident decisions.
In this paper, Bayesian Convolutional Neural Network (BayesCNN) using
Variational Inference is proposed, that introduces probability distribution
over the weights. Furthermore, the proposed BayesCNN architecture is applied to
tasks like Image Classification, Image Super-Resolution and Generative
Adversarial Networks. The results are compared to point-estimates based
architectures on MNIST, CIFAR-10 and CIFAR-100 datasets for Image
CLassification task, on BSD300 dataset for Image Super Resolution task and on
CIFAR10 dataset again for Generative Adversarial Network task.
BayesCNN is based on Bayes by Backprop which derives a variational
approximation to the true posterior. We, therefore, introduce the idea of
applying two convolutional operations, one for the mean and one for the
variance. Our proposed method not only achieves performances equivalent to
frequentist inference in identical architectures but also incorporate a
measurement for uncertainties and regularisation. It further eliminates the use
of dropout in the model. Moreover, we predict how certain the model prediction
is based on the epistemic and aleatoric uncertainties and empirically show how
the uncertainty can decrease, allowing the decisions made by the network to
become more deterministic as the training accuracy increases. Finally, we
propose ways to prune the Bayesian architecture and to make it more
computational and time effective. | [
"cs.LG",
"stat.ML"
] |
We aim to tackle a novel vision task called Weakly Supervised Visual Relation
Detection (WSVRD) to detect "subject-predicate-object" relations in an image
with object relation groundtruths available only at the image level. This is
motivated by the fact that it is extremely expensive to label the combinatorial
relations between objects at the instance level. Compared to the extensively
studied problem, Weakly Supervised Object Detection (WSOD), WSVRD is more
challenging as it needs to examine a large set of regions pairs, which is
computationally prohibitive and more likely stuck in a local optimal solution
such as those involving wrong spatial context. To this end, we present a
Parallel, Pairwise Region-based, Fully Convolutional Network (PPR-FCN) for
WSVRD. It uses a parallel FCN architecture that simultaneously performs pair
selection and classification of single regions and region pairs for object and
relation detection, while sharing almost all computation shared over the entire
image. In particular, we propose a novel position-role-sensitive score map with
pairwise RoI pooling to efficiently capture the crucial context associated with
a pair of objects. We demonstrate the superiority of PPR-FCN over all baselines
in solving the WSVRD challenge by using results of extensive experiments over
two visual relation benchmarks. | [
"cs.CV"
] |
Time series forecasting is an important problem across many domains,
including predictions of solar plant energy output, electricity consumption,
and traffic jam situation. In this paper, we propose to tackle such forecasting
problem with Transformer [1]. Although impressed by its performance in our
preliminary study, we found its two major weaknesses: (1) locality-agnostics:
the point-wise dot-product self-attention in canonical Transformer architecture
is insensitive to local context, which can make the model prone to anomalies in
time series; (2) memory bottleneck: space complexity of canonical Transformer
grows quadratically with sequence length $L$, making directly modeling long
time series infeasible. In order to solve these two issues, we first propose
convolutional self-attention by producing queries and keys with causal
convolution so that local context can be better incorporated into attention
mechanism. Then, we propose LogSparse Transformer with only $O(L(\log L)^{2})$
memory cost, improving forecasting accuracy for time series with fine
granularity and strong long-term dependencies under constrained memory budget.
Our experiments on both synthetic data and real-world datasets show that it
compares favorably to the state-of-the-art. | [
"cs.LG",
"stat.ML"
] |
We solve a challenging yet practically useful variant of 3D Bin Packing
Problem (3D-BPP). In our problem, the agent has limited information about the
items to be packed into the bin, and an item must be packed immediately after
its arrival without buffering or readjusting. The item's placement also
subjects to the constraints of collision avoidance and physical stability. We
formulate this online 3D-BPP as a constrained Markov decision process. To solve
the problem, we propose an effective and easy-to-implement constrained deep
reinforcement learning (DRL) method under the actor-critic framework. In
particular, we introduce a feasibility predictor to predict the feasibility
mask for the placement actions and use it to modulate the action probabilities
output by the actor during training. Such supervisions and transformations to
DRL facilitate the agent to learn feasible policies efficiently. Our method can
also be generalized e.g., with the ability to handle lookahead or items with
different orientations. We have conducted extensive evaluation showing that the
learned policy significantly outperforms the state-of-the-art methods. A user
study suggests that our method attains a human-level performance. | [
"cs.LG",
"stat.ML"
] |
We present to recover the complete 3D facial geometry from a single depth
view by proposing an Attention Guided Generative Adversarial Networks (AGGAN).
In contrast to existing work which normally requires two or more depth views to
recover a full 3D facial geometry, the proposed AGGAN is able to generate a
dense 3D voxel grid of the face from a single unconstrained depth view.
Specifically, AGGAN encodes the 3D facial geometry within a voxel space and
utilizes an attention-guided GAN to model the illposed 2.5D depth-3D mapping.
Multiple loss functions, which enforce the 3D facial geometry consistency,
together with a prior distribution of facial surface points in voxel space are
incorporated to guide the training process. Both qualitative and quantitative
comparisons show that AGGAN recovers a more complete and smoother 3D facial
shape, with the capability to handle a much wider range of view angles and
resist to noise in the depth view than conventional methods | [
"cs.CV"
] |
Financial forecasting is challenging and attractive in machine learning.
There are many classic solutions, as well as many deep learning based methods,
proposed to deal with it yielding encouraging performance. Stock time series
forecasting is the most representative problem in financial forecasting. Due to
the strong connections among stocks, the information valuable for forecasting
is not only included in individual stocks, but also included in the stocks
related to them. However, most previous works focus on one single stock, which
easily ignore the valuable information in others. To leverage more information,
in this paper, we propose a jointly forecasting approach to process multiple
time series of related stocks simultaneously, using multi-task learning
framework. Compared to the previous works, we use multiple networks to forecast
multiple related stocks, using the shared and private information of them
simultaneously through multi-task learning. Moreover, we propose an attention
method learning an optimized weighted combination of shared and private
information based on the idea of Capital Asset Pricing Model (CAPM) to help
forecast. Experimental results on various data show improved forecasting
performance over baseline methods. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Virtual screening can accelerate drug discovery by identifying promising
candidates for experimental evaluation. Machine learning is a powerful method
for screening, as it can learn complex structure-property relationships from
experimental data and make rapid predictions over virtual libraries. Molecules
inherently exist as a three-dimensional ensemble and their biological action
typically occurs through supramolecular recognition. However, most deep
learning approaches to molecular property prediction use a 2D graph
representation as input, and in some cases a single 3D conformation. Here we
investigate how the 3D information of multiple conformers, traditionally known
as 4D information in the cheminformatics community, can improve molecular
property prediction in deep learning models. We introduce multiple deep
learning models that expand upon key architectures such as ChemProp and Schnet,
adding elements such as multiple-conformer inputs and conformer attention. We
then benchmark the performance trade-offs of these models on 2D, 3D and 4D
representations in the prediction of drug activity using a large training set
of geometrically resolved molecules. The new architectures perform
significantly better than 2D models, but their performance is often just as
strong with a single conformer as with many. We also find that 4D deep learning
models learn interpretable attention weights for each conformer. | [
"cs.LG",
"physics.chem-ph"
] |
The key to overcome class imbalance problems is to capture the distribution
of minority class accurately. Generative Adversarial Networks (GANs) have shown
some potentials to tackle class imbalance problems due to their capability of
reproducing data distributions given ample training data samples. However, the
scarce samples of one or more classes still pose a great challenge for GANs to
learn accurate distributions for the minority classes. In this work, we propose
an Annealing Genetic GAN (AGGAN) method, which aims to reproduce the
distributions closest to the ones of the minority classes using only limited
data samples. Our AGGAN renovates the training of GANs as an evolutionary
process that incorporates the mechanism of simulated annealing. In particular,
the generator uses different training strategies to generate multiple offspring
and retain the best. Then, we use the Metropolis criterion in the simulated
annealing to decide whether we should update the best offspring for the
generator. As the Metropolis criterion allows a certain chance to accept the
worse solutions, it enables our AGGAN steering away from the local optimum.
According to both theoretical analysis and experimental studies on multiple
imbalanced image datasets, we prove that the proposed training strategy can
enable our AGGAN to reproduce the distributions of minority classes from scarce
samples and provide an effective and robust solution for the class imbalance
problem. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
In this report we describe the technical details of our submission to the
EPIC-Kitchens Action Recognition 2020 Challenge. To participate in the
challenge we deployed spatio-temporal feature extraction and aggregation models
we have developed recently: Gate-Shift Module (GSM) [1] and EgoACO, an
extension of Long Short-Term Attention (LSTA) [2]. We design an ensemble of GSM
and EgoACO model families with different backbones and pre-training to generate
the prediction scores. Our submission, visible on the public leaderboard with
team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 40.0% on
S1 setting, and 25.71% on S2 setting, using only RGB. | [
"cs.CV"
] |
Shapley values have become increasingly popular in the machine learning
literature thanks to their attractive axiomatisation, flexibility, and
uniqueness in satisfying certain notions of `fairness'. The flexibility arises
from the myriad potential forms of the Shapley value \textit{game formulation}.
Amongst the consequences of this flexibility is that there are now many types
of Shapley values being discussed, with such variety being a source of
potential misunderstanding. To the best of our knowledge, all existing game
formulations in the machine learning and statistics literature fall into a
category which we name the model-dependent category of game formulations. In
this work, we consider an alternative and novel formulation which leads to the
first instance of what we call model-independent Shapley values. These Shapley
values use a (non-parametric) measure of non-linear dependence as the
characteristic function. The strength of these Shapley values is in their
ability to uncover and attribute non-linear dependencies amongst features. We
introduce and demonstrate the use of the energy distance correlations,
affine-invariant distance correlation, and Hilbert-Shmidt independence
criterion as Shapley value characteristic functions. In particular, we
demonstrate their potential value for exploratory data analysis and model
diagnostics. We conclude with an interesting expository application to a
classical medical survey data set. | [
"stat.ML",
"cs.LG"
] |
In this paper, we proposed a novel mutual consistency network (MC-Net+) to
effectively exploit the unlabeled hard regions for semi-supervised medical
image segmentation. The MC-Net+ model is motivated by the observation that deep
models trained with limited annotations are prone to output highly uncertain
and easily mis-classified predictions in the ambiguous regions (e.g. adhesive
edges or thin branches) for the image segmentation task. Leveraging these
region-level challenging samples can make the semi-supervised segmentation
model training more effective. Therefore, our proposed MC-Net+ model consists
of two new designs. First, the model contains one shared encoder and multiple
sightly different decoders (i.e. using different up-sampling strategies). The
statistical discrepancy of multiple decoders' outputs is computed to denote the
model's uncertainty, which indicates the unlabeled hard regions. Second, a new
mutual consistency constraint is enforced between one decoder's probability
output and other decoders' soft pseudo labels. In this way, we minimize the
model's uncertainty during training and force the model to generate invariant
and low-entropy results in such challenging areas of unlabeled data, in order
to learn a generalized feature representation. We compared the segmentation
results of the MC-Net+ with five state-of-the-art semi-supervised approaches on
three public medical datasets. Extension experiments with two common
semi-supervised settings demonstrate the superior performance of our model over
other existing methods, which sets a new state of the art for semi-supervised
medical image segmentation. | [
"cs.CV",
"cs.AI"
] |
Skin conditions are reported the 4th leading cause of nonfatal disease burden
worldwide. However, given the colossal spectrum of skin disorders defined
clinically and shortage in dermatology expertise, diagnosing skin conditions in
a timely and accurate manner remains a challenging task. Using computer vision
technologies, a deep learning system has proven effective assisting clinicians
in image diagnostics of radiology, ophthalmology and more. In this paper, we
propose a deep learning system (DLS) that may predict differential diagnosis of
skin conditions using clinical images. Our DLS formulates the differential
diagnostics as a multi-label classification task over 80 conditions when only
incomplete image labels are available. We tackle the label incompleteness
problem by combining a classification network with a Graph Convolutional
Network (GCN) that characterizes label co-occurrence and effectively
regularizes it towards a sparse representation. Our approach is demonstrated on
136,462 clinical images and concludes that the classification accuracy greatly
benefit from the Co-occurrence supervision. Our DLS achieves 93.6% top-5
accuracy on 12,378 test images and consistently outperform the baseline
classification network. | [
"cs.CV"
] |
Flow-based deep generative models learn data distributions by transforming a
simple base distribution into a complex distribution via a set of invertible
transformations. Due to the invertibility, such models can score unseen data
samples by computing their exact likelihood under the learned distribution.
This makes flow-based models a perfect tool for novelty detection, an anomaly
detection technique where unseen data samples are classified as normal or
abnormal by scoring them against a learned model of normal data. We show that
normalizing flows can be used as novelty detectors in time series. Two
flow-based models, Masked Autoregressive Flows and Free-form Jacobian of
Reversible Dynamics restricted by autoregressive MADE networks, are tested on
synthetic data and motor current data from an industrial machine and achieve
good results, outperforming a conventional novelty detection method, the Local
Outlier Factor. | [
"cs.LG",
"stat.ML"
] |
Curriculum learning (CL) is a training strategy that trains a machine
learning model from easier data to harder data, which imitates the meaningful
learning order in human curricula. As an easy-to-use plug-in, the CL strategy
has demonstrated its power in improving the generalization capacity and
convergence rate of various models in a wide range of scenarios such as
computer vision and natural language processing etc. In this survey article, we
comprehensively review CL from various aspects including motivations,
definitions, theories, and applications. We discuss works on curriculum
learning within a general CL framework, elaborating on how to design a manually
predefined curriculum or an automatic curriculum. In particular, we summarize
existing CL designs based on the general framework of Difficulty
Measurer+Training Scheduler and further categorize the methodologies for
automatic CL into four groups, i.e., Self-paced Learning, Transfer Teacher, RL
Teacher, and Other Automatic CL. We also analyze principles to select different
CL designs that may benefit practical applications. Finally, we present our
insights on the relationships connecting CL and other machine learning concepts
including transfer learning, meta-learning, continual learning and active
learning, etc., then point out challenges in CL as well as potential future
research directions deserving further investigations. | [
"cs.LG",
"cs.AI"
] |
As GAN-based video and image manipulation technologies become more
sophisticated and easily accessible, there is an urgent need for effective
deepfake detection technologies. Moreover, various deepfake generation
techniques have emerged over the past few years. While many deepfake detection
methods have been proposed, their performance suffers from new types of
deepfake methods on which they are not sufficiently trained. To detect new
types of deepfakes, the model should learn from additional data without losing
its prior knowledge about deepfakes (catastrophic forgetting), especially when
new deepfakes are significantly different. In this work, we employ the
Representation Learning (ReL) and Knowledge Distillation (KD) paradigms to
introduce a transfer learning-based Feature Representation Transfer Adaptation
Learning (FReTAL) method. We use FReTAL to perform domain adaptation tasks on
new deepfake datasets while minimizing catastrophic forgetting. Our student
model can quickly adapt to new types of deepfake by distilling knowledge from a
pre-trained teacher model and applying transfer learning without using source
domain data during domain adaptation. Through experiments on FaceForensics++
datasets, we demonstrate that FReTAL outperforms all baselines on the domain
adaptation task with up to 86.97% accuracy on low-quality deepfakes. | [
"cs.CV",
"I.4.9; I.5.4"
] |
Recently, a lot of attention has been focused on the incorporation of 3D data
into face analysis and its applications. Despite providing a more accurate
representation of the face, 3D facial images are more complex to acquire than
2D pictures. As a consequence, great effort has been invested in developing
systems that reconstruct 3D faces from an uncalibrated 2D image. However, the
3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is
needed to restrict the solutions space. In this work, we review 3D face
reconstruction methods proposed in the last decade, focusing on those that only
use 2D pictures captured under uncontrolled conditions. We present a
classification of the proposed methods based on the technique used to add prior
knowledge, considering three main strategies, namely, statistical model
fitting, photometry, and deep learning, and reviewing each of them separately.
In addition, given the relevance of statistical 3D facial models as prior
knowledge, we explain the construction procedure and provide a list of the most
popular publicly available 3D facial models. After the exhaustive study of
3D-from-2D face reconstruction approaches, we observe that the deep learning
strategy is rapidly growing since the last few years, becoming the standard
choice in replacement of the widespread statistical model fitting. Unlike the
other two strategies, photometry-based methods have decreased in number due to
the need for strong underlying assumptions that limit the quality of their
reconstructions compared to statistical model fitting and deep learning
methods. The review also identifies current challenges and suggests avenues for
future research. | [
"cs.CV"
] |
There is a surge of interest in image scene graph generation (object,
attribute and relationship detection) due to the need of building fine-grained
image understanding models that go beyond object detection. Due to the lack of
a good benchmark, the reported results of different scene graph generation
models are not directly comparable, impeding the research progress. We have
developed a much-needed scene graph generation benchmark based on the
maskrcnn-benchmark and several popular models. This paper presents main
features of our benchmark and a comprehensive ablation study of scene graph
generation models using the Visual Genome and OpenImages Visual relationship
detection datasets. Our codebase is made publicly available at
https://github.com/microsoft/scene_graph_benchmark. | [
"cs.CV"
] |
Deep networks allow to obtain outstanding results in semantic segmentation,
however they need to be trained in a single shot with a large amount of data.
Continual learning settings where new classes are learned in incremental steps
and previous training data is no longer available are challenging due to the
catastrophic forgetting phenomenon. Existing approaches typically fail when
several incremental steps are performed or in presence of a distribution shift
of the background class. We tackle these issues by recreating no longer
available data for the old classes and outlining a content inpainting scheme on
the background class. We propose two sources for replay data. The first resorts
to a generative adversarial network to sample from the class space of past
learning steps. The second relies on web-crawled data to retrieve images
containing examples of old classes from online databases. In both scenarios no
samples of past steps are stored, thus avoiding privacy concerns. Replay data
are then blended with new samples during the incremental steps. Our approach,
RECALL, outperforms state-of-the-art methods. | [
"cs.CV"
] |
Attention and self-attention mechanisms, are now central to state-of-the-art
deep learning on sequential tasks. However, most recent progress hinges on
heuristic approaches with limited understanding of attention's role in model
optimization and computation, and rely on considerable memory and computational
resources that scale poorly. In this work, we present a formal analysis of how
self-attention affects gradient propagation in recurrent networks, and prove
that it mitigates the problem of vanishing gradients when trying to capture
long-term dependencies by establishing concrete bounds for gradient norms.
Building on these results, we propose a relevancy screening mechanism, inspired
by the cognitive process of memory consolidation, that allows for a scalable
use of sparse self-attention with recurrence. While providing guarantees to
avoid vanishing gradients, we use simple numerical experiments to demonstrate
the tradeoffs in performance and computational resources by efficiently
balancing attention and recurrence. Based on our results, we propose a concrete
direction of research to improve scalability of attentive networks. | [
"cs.LG",
"stat.ML"
] |
We consider the problem of approximate Bayesian inference in log-supermodular
models. These models encompass regular pairwise MRFs with binary variables, but
allow to capture high-order interactions, which are intractable for existing
approximate inference techniques such as belief propagation, mean field, and
variants. We show that a recently proposed variational approach to inference in
log-supermodular models -L-FIELD- reduces to the widely-studied minimum norm
problem for submodular minimization. This insight allows to leverage powerful
existing tools, and hence to solve the variational problem orders of magnitude
more efficiently than previously possible. We then provide another natural
interpretation of L-FIELD, demonstrating that it exactly minimizes a specific
type of R\'enyi divergence measure. This insight sheds light on the nature of
the variational approximations produced by L-FIELD. Furthermore, we show how to
perform parallel inference as message passing in a suitable factor graph at a
linear convergence rate, without having to sum up over all the configurations
of the factor. Finally, we apply our approach to a challenging image
segmentation task. Our experiments confirm scalability of our approach, high
quality of the marginals, and the benefit of incorporating higher-order
potentials. | [
"cs.LG",
"stat.ML"
] |
Many recent successful (deep) reinforcement learning algorithms make use of
regularization, generally based on entropy or Kullback-Leibler divergence. We
propose a general theory of regularized Markov Decision Processes that
generalizes these approaches in two directions: we consider a larger class of
regularizers, and we consider the general modified policy iteration approach,
encompassing both policy iteration and value iteration. The core building
blocks of this theory are a notion of regularized Bellman operator and the
Legendre-Fenchel transform, a classical tool of convex optimization. This
approach allows for error propagation analyses of general algorithmic schemes
of which (possibly variants of) classical algorithms such as Trust Region
Policy Optimization, Soft Q-learning, Stochastic Actor Critic or Dynamic Policy
Programming are special cases. This also draws connections to proximal convex
optimization, especially to Mirror Descent. | [
"cs.LG",
"stat.ML"
] |
Presently, deep learning technology has been widely used in the field of
image recognition. However, it mainly aims at the recognition and detection of
ordinary pictures and common scenes. As special images, remote sensing images
have different shooting angles and shooting methods compared with ordinary
ones, which makes remote sensing images play an irreplaceable role in some
areas. In this paper, based on a deep convolution neural network for providing
multi-level information of images and combines RPN (Region Proposal Network)
for generating multi-angle ROIs (Region of Interest), a new model for object
detection and recognition in remote sensing images is proposed. In the
experiment, it achieves better results than traditional ways, which demonstrate
that the model proposed here would have a huge potential application in remote
sensing image recognition. | [
"cs.CV",
"cs.NE",
"eess.IV"
] |
Delineation approaches provide significant benefits to various domains,
including agriculture, environmental and natural disasters monitoring. Most of
the work in the literature utilize traditional segmentation methods that
require a large amount of computational and storage resources. Deep learning
has transformed computer vision and dramatically improved machine translation,
though it requires massive dataset for training and significant resources for
inference. More importantly, energy-efficient embedded vision hardware
delivering real-time and robust performance is crucial in the aforementioned
application. In this work, we propose a U-Net based tree delineation method,
which is effectively trained using multi-spectral imagery but can then
delineate single-spectrum images. The deep architecture that also performs
localization, i.e., a class label corresponds to each pixel, has been
successfully used to allow training with a small set of segmented images. The
ground truth data were generated using traditional image denoising and
segmentation approaches. To be able to execute the proposed DNN efficiently in
embedded platforms designed for deep learning approaches, we employ traditional
model compression and acceleration methods. Extensive evaluation studies using
data collected from UAVs equipped with multi-spectral cameras demonstrate the
effectiveness of the proposed methods in terms of delineation accuracy and
execution efficiency. | [
"cs.CV",
"eess.IV"
] |
Prediction of spatio-temporal chaotic systems is important in various fields,
such as Numerical Weather Prediction (NWP). While data assimilation methods
have been applied in NWP, machine learning techniques, such as Reservoir
Computing (RC), are recently recognized as promising tools to predict
spatio-temporal chaotic systems. However, the sensitivity of the skill of the
machine learning based prediction to the imperfectness of observations is
unclear. In this study, we evaluate the skill of RC with noisy and sparsely
distributed observations. We intensively compare the performances of RC and
Local Ensemble Transform Kalman Filter (LETKF) by applying them to the
prediction of the Lorenz 96 system. Although RC can successfully predict the
Lorenz 96 system if the system is perfectly observed, we find that RC is
vulnerable to observation sparsity compared with LETKF. To overcome this
limitation of RC, we propose to combine LETKF and RC. In our proposed method,
the system is predicted by RC that learned the analysis time series estimated
by LETKF. Our proposed method can successfully predict the Lorenz 96 system
using noisy and sparsely distributed observations. Most importantly, our method
can predict better than LETKF when the process-based model is imperfect. | [
"cs.LG",
"physics.comp-ph",
"stat.ML"
] |
A basic question in learning theory is to identify if two distributions are
identical when we have access only to examples sampled from the distributions.
This basic task is considered, for example, in the context of Generative
Adversarial Networks (GANs), where a discriminator is trained to distinguish
between a real-life distribution and a synthetic distribution. % Classically,
we use a hypothesis class $H$ and claim that the two distributions are distinct
if for some $h\in H$ the expected value on the two distributions is
(significantly) different. Our starting point is the following fundamental
problem: "is having the hypothesis dependent on more than a single random
example beneficial". To address this challenge we define $k$-ary based
discriminators, which have a family of Boolean $k$-ary functions $\mathcal{G}$.
Each function $g\in \mathcal{G}$ naturally defines a hyper-graph, indicating
whether a given hyper-edge exists. A function $g\in \mathcal{G}$ distinguishes
between two distributions, if the expected value of $g$, on a $k$-tuple of
i.i.d examples, on the two distributions is (significantly) different. We study
the expressiveness of families of $k$-ary functions, compared to the classical
hypothesis class $H$, which is $k=1$. We show a separation in expressiveness of
$k+1$-ary versus $k$-ary functions. This demonstrate the great benefit of
having $k\geq 2$ as distinguishers. For $k\geq 2$ we introduce a notion similar
to the VC-dimension, and show that it controls the sample complexity. We
proceed and provide upper and lower bounds as a function of our extended notion
of VC-dimension. | [
"cs.LG",
"stat.ML"
] |
To efficiently run DNNs on the edge/cloud, many new DNN inference
accelerators are being designed and deployed frequently. To enhance the
resource efficiency of DNNs, model quantization is a widely-used approach.
However, different accelerator/HW has different resources leading to the need
for specialized quantization strategy of each HW. Moreover, using the same
quantization for every layer may be sub-optimal, increasing the designspace of
possible quantization choices. This makes manual-tuning infeasible. Recent work
in automatically determining quantization for each layer is driven by
optimization methods such as reinforcement learning. However, these approaches
need re-training the RL for every new HW platform. We propose a new way for
autonomous quantization and HW-aware tuning. We propose a generative model,
AQGAN, which takes a target accuracy as the condition and generates a suite of
quantization configurations. With the conditional generative model, the user
can autonomously generate different configurations with different targets in
inference time. Moreover, we propose a simplified HW-tuning flow, which uses
the generative model to generate proposals and execute simple selection based
on the HW resource budget, whose process is fast and interactive. We evaluate
our model on five of the widely-used efficient models on the ImageNet dataset.
We compare with existing uniform quantization and state-of-the-art autonomous
quantization methods. Our generative model shows competitive achieved accuracy,
however, with around two degrees less search cost for each design point. Our
generative model shows the generated quantization configuration can lead to
less than 3.5% error across all experiments. | [
"cs.LG",
"stat.ML"
] |
This thesis presents methods and approaches to image color correction, color
enhancement, and color editing. To begin, we study the color correction problem
from the standpoint of the camera's image signal processor (ISP). A camera's
ISP is hardware that applies a series of in-camera image processing and color
manipulation steps, many of which are nonlinear in nature, to render the
initial sensor image to its final photo-finished representation saved in the
8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the
major procedures applied by the ISP for color correction, this thesis presents
two different methods for ISP white balancing. Afterward, we discuss another
scenario of correcting and editing image colors, where we present a set of
methods to correct and edit WB settings for images that have been improperly
white-balanced by the ISP. Then, we explore another factor that has a
significant impact on the quality of camera-rendered colors, in which we
outline two different methods to correct exposure errors in camera-rendered
images. Lastly, we discuss post-capture auto color editing and manipulation. In
particular, we propose auto image recoloring methods to generate different
realistic versions of the same camera-rendered image with new colors. Through
extensive evaluations, we demonstrate that our methods provide superior
solutions compared to existing alternatives targeting color correction, color
enhancement, and color editing. | [
"cs.CV"
] |
Thumbnail is the face of online videos. The explosive growth of videos both
in number and variety underpins the importance of a good thumbnail because it
saves potential viewers time to choose videos and even entice them to click on
them. A good thumbnail should be a frame that best represents the content of a
video while at the same time capturing viewers' attention. However, the
techniques and models in the past only focus on frames within a video, and we
believe such narrowed focus leave out much useful information that are part of
a video. In this paper, we expand the definition of content to include title,
description, and audio of a video and utilize information provided by these
modalities in our selection model. Specifically, our model will first sample
frames uniformly in time and return the top 1,000 frames in this subset with
the highest aesthetic scores by a Double-column Convolutional Neural Network,
to avoid the computational burden of processing all frames in downstream task.
Then, the model incorporates frame features extracted from VGG16, text features
from ELECTRA, and audio features from TRILL. These models were selected because
of their results on popular datasets as well as their competitive performances.
After feature extraction, the time-series features, frames and audio, will be
fed into Transformer encoder layers to return a vector representing their
corresponding modality. Each of the four features (frames, title, description,
audios) will pass through a context gating layer before concatenation. Finally,
our model will generate a vector in the latent space and select the frame that
is most similar to this vector in the latent space. To the best of our
knowledge, we are the first to propose a multi-modal deep learning model to
select video thumbnail, which beats the result from the previous
State-of-The-Art models. | [
"cs.CV",
"cs.AI"
] |
Time series forecasting is an important yet challenging task. Though deep
learning methods have recently been developed to give superior forecasting
results, it is crucial to improve the interpretability of time series models.
Previous interpretation methods, including the methods for general neural
networks and attention-based methods, mainly consider the interpretation in the
feature dimension while ignoring the crucial temporal dimension. In this paper,
we present the series saliency framework for temporal interpretation for
multivariate time series forecasting, which considers the forecasting
interpretation in both feature and temporal dimensions. By extracting the
"series images" from the sliding windows of the time series, we apply the
saliency map segmentation following the smallest destroying region principle.
The series saliency framework can be employed to any well-defined deep learning
models and works as a data augmentation to get more accurate forecasts.
Experimental results on several real datasets demonstrate that our framework
generates temporal interpretations for the time series forecasting task while
produces accurate time series forecast. | [
"cs.LG",
"cs.AI"
] |
Deep hashing methods have been proved to be effective for the large-scale
medical image search assisting reference-based diagnosis for clinicians.
However, when the salient region plays a maximal discriminative role in
ophthalmic image, existing deep hashing methods do not fully exploit the
learning ability of the deep network to capture the features of salient regions
pointedly. The different grades or classes of ophthalmic images may be share
similar overall performance but have subtle differences that can be
differentiated by mining salient regions. To address this issue, we propose a
novel end-to-end network, named Attention-based Saliency Hashing (ASH), for
learning compact hash-code to represent ophthalmic images. ASH embeds a
spatial-attention module to focus more on the representation of salient regions
and highlights their essential role in differentiating ophthalmic images.
Benefiting from the spatial-attention module, the information of salient
regions can be mapped into the hash-code for similarity calculation. In the
training stage, we input the image pairs to share the weights of the network,
and a pairwise loss is designed to maximize the discriminability of the
hash-code. In the retrieval stage, ASH obtains the hash-code by inputting an
image with an end-to-end manner, then the hash-code is used to similarity
calculation to return the most similar images. Extensive experiments on two
different modalities of ophthalmic image datasets demonstrate that the proposed
ASH can further improve the retrieval performance compared to the
state-of-the-art deep hashing methods due to the huge contributions of the
spatial-attention module. | [
"cs.CV",
"cs.AI"
] |
Due to the absorption and scattering effects of the water, underwater images
tend to suffer from many severe problems, such as low contrast, grayed out
colors and blurring content. To improve the visual quality of underwater
images, we proposed a novel enhancement model, which is a trainable end-to-end
neural model. Two parts constitute the overall model. The first one is a
non-parameter layer for the preliminary color correction, then the second part
is consisted of parametric layers for a self-adaptive refinement, namely the
channel-wise linear shift. For better details, contrast and colorfulness, this
enhancement network is jointly optimized by the pixel-level and
characteristiclevel training criteria. Through extensive experiments on natural
underwater scenes, we show that the proposed method can get high quality
enhancement results. | [
"cs.CV"
] |
Augmented Reality and mobile robots are gaining much attention within
industries due to the high potential to make processes cost and time efficient.
To facilitate augmented reality, a calibration between the Augmented Reality
device and the environment is necessary. This is a challenge when dealing with
mobile robots due to the mobility of all entities making the environment
dynamic. On this account, we propose a novel approach to calibrate the
Augmented Reality device using 3D depth sensor data. We use the depth camera of
a cutting edge Augmented Reality Device - the Microsoft Hololens for deep
learning based calibration. Therefore, we modified a neural network based on
the recently published VoteNet architecture which works directly on the point
cloud input observed by the Hololens. We achieve satisfying results and
eliminate external tools like markers, thus enabling a more intuitive and
flexible work flow for Augmented Reality integration. The results are adaptable
to work with all depth cameras and are promising for further research.
Furthermore, we introduce an open source 3D point cloud labeling tool, which is
to our knowledge the first open source tool for labeling raw point cloud data. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
Photorealistic style transfer aims to transfer the style of one image to
another, but preserves the original structure and detail outline of the content
image, which makes the content image still look like a real shot after the
style transfer. Although some realistic image styling methods have been
proposed, these methods are vulnerable to lose the details of the content image
and produce some irregular distortion structures. In this paper, we use a
high-resolution network as the image generation network. Compared to other
methods, which reduce the resolution and then restore the high resolution, our
generation network maintains high resolution throughout the process. By
connecting high-resolution subnets to low-resolution subnets in parallel and
repeatedly multi-scale fusion, high-resolution subnets can continuously receive
information from low-resolution subnets. This allows our network to discard
less information contained in the image, so the generated images may have a
more elaborate structure and less distortion, which is crucial to the visual
quality. We conducted extensive experiments and compared the results with
existing methods. The experimental results show that our model is effective and
produces better results than existing methods for photorealistic image
stylization. Our source code with PyTorch framework will be publicly available
at https://github.com/limingcv/Photorealistic-Style-Transfer | [
"cs.CV",
"eess.IV"
] |
We present a novel method to solve image analogy problems : it allows to
learn the relation between paired images present in training data, and then
generalize and generate images that correspond to the relation, but were never
seen in the training set. Therefore, we call the method Conditional Analogy
Generative Adversarial Network (CAGAN), as it is based on adversarial training
and employs deep convolutional neural networks. An especially interesting
application of that technique is automatic swapping of clothing on fashion
model photos. Our work has the following contributions. First, the definition
of the end-to-end trainable CAGAN architecture, which implicitly learns
segmentation masks without expensive supervised labeling data. Second,
experimental results show plausible segmentation masks and often convincing
swapped images, given the target article. Finally, we discuss the next steps
for that technique: neural network architecture improvements and more advanced
applications. | [
"stat.ML",
"cs.AI",
"cs.CV"
] |
State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms
typically act by iteratively solving empirical models, i.e., by performing
\emph{full-planning} on Markov Decision Processes (MDPs) built by the gathered
experience. In this paper, we focus on model-based RL in the finite-state
finite-horizon MDP setting and establish that exploring with \emph{greedy
policies} -- act by \emph{1-step planning} -- can achieve tight minimax
performance in terms of regret, $\tilde{\mathcal{O}}(\sqrt{HSAT})$. Thus,
full-planning in model-based RL can be avoided altogether without any
performance degradation, and, by doing so, the computational complexity
decreases by a factor of $S$. The results are based on a novel analysis of
real-time dynamic programming, then extended to model-based RL. Specifically,
we generalize existing algorithms that perform full-planning to such that act
by 1-step planning. For these generalizations, we prove regret bounds with the
same rate as their full-planning counterparts. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Recent research on deep neural networks has focused primarily on improving
accuracy. For a given accuracy level, it is typically possible to identify
multiple DNN architectures that achieve that accuracy level. With equivalent
accuracy, smaller DNN architectures offer at least three advantages: (1)
Smaller DNNs require less communication across servers during distributed
training. (2) Smaller DNNs require less bandwidth to export a new model from
the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on
FPGAs and other hardware with limited memory. To provide all of these
advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet
achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
Additionally, with model compression techniques we are able to compress
SqueezeNet to less than 0.5MB (510x smaller than AlexNet).
The SqueezeNet architecture is available for download here:
https://github.com/DeepScale/SqueezeNet | [
"cs.CV",
"cs.AI"
] |
Graph embedding, aiming to learn low-dimensional representations (aka.
embeddings) of nodes, has received significant attention recently. Recent years
have witnessed a surge of efforts made on static graphs, among which Graph
Convolutional Network (GCN) has emerged as an effective class of models.
However, these methods mainly focus on the static graph embedding. In this
work, we propose an efficient dynamic graph embedding approach, Dynamic Graph
Convolutional Network (DyGCN), which is an extension of GCN-based methods. We
naturally generalizes the embedding propagation scheme of GCN to dynamic
setting in an efficient manner, which is to propagate the change along the
graph to update node embeddings. The most affected nodes are first updated, and
then their changes are propagated to the further nodes and leads to their
update. Extensive experiments conducted on various dynamic graphs demonstrate
that our model can update the node embeddings in a time-saving and
performance-preserving way. | [
"cs.LG",
"cs.IR"
] |
Social media platforms like Facebook, Twitter, and Instagram have enabled
connection and communication on a large scale. It has revolutionized the rate
at which information is shared and enhanced its reach. However, another side of
the coin dictates an alarming story. These platforms have led to an increase in
the creation and spread of fake news. The fake news has not only influenced
people in the wrong direction but also claimed human lives. During these
critical times of the Covid19 pandemic, it is easy to mislead people and make
them believe in fatal information. Therefore it is important to curb fake news
at source and prevent it from spreading to a larger audience. We look at
automated techniques for fake news detection from a data mining perspective. We
evaluate different supervised text classification algorithms on Contraint@AAAI
2021 Covid-19 Fake news detection dataset. The classification algorithms are
based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM),
and Bidirectional Encoder Representations from Transformers (BERT). We also
evaluate the importance of unsupervised learning in the form of language model
pre-training and distributed word representations using unlabelled covid tweets
corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news
detection dataset. | [
"cs.LG",
"cs.IR"
] |
Machine Learning models become increasingly proficient in complex tasks.
However, even for experts in the field, it can be difficult to understand what
the model learned. This hampers trust and acceptance, and it obstructs the
possibility to correct the model. There is therefore a need for transparency of
machine learning models. The development of transparent classification models
has received much attention, but there are few developments for achieving
transparent Reinforcement Learning (RL) models. In this study we propose a
method that enables a RL agent to explain its behavior in terms of the expected
consequences of state transitions and outcomes. First, we define a translation
of states and actions to a description that is easier to understand for human
users. Second, we developed a procedure that enables the agent to obtain the
consequences of a single action, as well as its entire policy. The method
calculates contrasts between the consequences of a policy derived from a user
query, and of the learned policy of the agent. Third, a format for generating
explanations was constructed. A pilot survey study was conducted to explore
preferences of users for different explanation properties. Results indicate
that human users tend to favor explanations about policy rather than about
single actions. | [
"cs.LG",
"stat.ML"
] |
Finding an interpretable non-redundant representation of real-world data is
one of the key problems in Machine Learning. Biological neural networks are
known to solve this problem quite well in unsupervised manner, yet unsupervised
artificial neural networks either struggle to do it or require fine tuning for
each task individually. We associate this with the fact that a biological brain
learns in the context of the relationships between observations, while an
artificial network does not. We also notice that, though a naive data
augmentation technique can be very useful for supervised learning problems,
autoencoders typically fail to generalize transformations from data
augmentations. Thus, we believe that providing additional knowledge about
relationships between data samples will improve model's capability of finding
useful inner data representation. More formally, we consider a dataset not as a
manifold, but as a category, where the examples are objects. Two these objects
are connected by a morphism, if they actually represent different
transformations of the same entity. Following this formalism, we propose a
novel method of using data augmentations when training autoencoders. We train a
Variational Autoencoder in such a way, that it makes transformation outcome
predictable by auxiliary network in terms of the hidden representation. We
believe that the classification accuracy of a linear classifier on the learned
representation is a good metric to measure its interpretability. In our
experiments, present approach outperforms $\beta$-VAE and is comparable with
Gaussian-mixture VAE. | [
"cs.LG",
"cs.CV"
] |
Tasks like image reconstruction in computer vision, matrix completion in
recommender systems and link prediction in graph theory, are well studied in
machine learning literature. In this work, we apply a denoising
autoencoder-based neural network architecture to the task of completing partial
multiplication (Cayley) tables of finite semigroups. We suggest a novel loss
function for that task based on the algebraic nature of the semigroup data. We
also provide a software package for conducting experiments similar to those
carried out in this work. Our experiments showed that with only about 10% of
the available data, it is possible to build a model capable of reconstructing a
full Cayley from only half of it in about 80% of cases. | [
"cs.LG"
] |
We introduce a novel self-supervised pretext task for learning
representations from audio-visual content. Prior work on audio-visual
representation learning leverages correspondences at the video level.
Approaches based on audio-visual correspondence (AVC) predict whether audio and
video clips originate from the same or different video instances. Audio-visual
temporal synchronization (AVTS) further discriminates negative pairs originated
from the same video instance but at different moments in time. While these
approaches learn high-quality representations for downstream tasks such as
action recognition, their training objectives disregard spatial cues naturally
occurring in audio and visual signals. To learn from these spatial cues, we
tasked a network to perform contrastive audio-visual spatial alignment of
360{\deg} video and spatial audio. The ability to perform spatial alignment is
enhanced by reasoning over the full spatial content of the 360{\deg} video
using a transformer architecture to combine representations from multiple
viewpoints. The advantages of the proposed pretext task are demonstrated on a
variety of audio and visual downstream tasks, including audio-visual
correspondence, spatial alignment, action recognition, and video semantic
segmentation. | [
"cs.CV"
] |
In this paper, we present InSeGAN, an unsupervised 3D generative adversarial
network (GAN) for segmenting (nearly) identical instances of rigid objects in
depth images. Using an analysis-by-synthesis approach, we design a novel GAN
architecture to synthesize a multiple-instance depth image with independent
control over each instance. InSeGAN takes in a set of code vectors (e.g.,
random noise vectors), each encoding the 3D pose of an object that is
represented by a learned implicit object template. The generator has two
distinct modules. The first module, the instance feature generator, uses each
encoded pose to transform the implicit template into a feature map
representation of each object instance. The second module, the depth image
renderer, aggregates all of the single-instance feature maps output by the
first module and generates a multiple-instance depth image. A discriminator
distinguishes the generated multiple-instance depth images from the
distribution of true depth images. To use our model for instance segmentation,
we propose an instance pose encoder that learns to take in a generated depth
image and reproduce the pose code vectors for all of the object instances. To
evaluate our approach, we introduce a new synthetic dataset, "Insta-10",
consisting of 100,000 depth images, each with 5 instances of an object from one
of 10 classes. Our experiments on Insta-10, as well as on real-world noisy
depth images, show that InSeGAN achieves state-of-the-art performance, often
outperforming prior methods by large margins. | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] |
Pseudo-rehearsal allows neural networks to learn a sequence of tasks without
forgetting how to perform in earlier tasks. Preventing forgetting is achieved
by introducing a generative network which can produce data from previously seen
tasks so that it can be rehearsed along side learning the new task. This has
been found to be effective in both supervised and reinforcement learning. Our
current work aims to further prevent forgetting by encouraging the generator to
accurately generate features important for task retention. More specifically,
the generator is improved by introducing a second discriminator into the
Generative Adversarial Network which learns to classify between real and fake
items from the intermediate activation patterns that they produce when fed
through a continual learning agent. Using Atari 2600 games, we experimentally
find that improving the generator can considerably reduce catastrophic
forgetting compared to the standard pseudo-rehearsal methods used in deep
reinforcement learning. Furthermore, we propose normalising the Q-values taught
to the long-term system as we observe this substantially reduces catastrophic
forgetting by minimising the interference between tasks' reward functions. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Animals exhibit an innate ability to learn regularities of the world through
interaction. By performing experiments in their environment, they are able to
discern the causal factors of variation and infer how they affect the world's
dynamics. Inspired by this, we attempt to equip reinforcement learning agents
with the ability to perform experiments that facilitate a categorization of the
rolled-out trajectories, and to subsequently infer the causal factors of the
environment in a hierarchical manner. We introduce {\em causal curiosity}, a
novel intrinsic reward, and show that it allows our agents to learn optimal
sequences of actions and discover causal factors in the dynamics of the
environment. The learned behavior allows the agents to infer a binary quantized
representation for the ground-truth causal factors in every environment.
Additionally, we find that these experimental behaviors are semantically
meaningful (e.g., our agents learn to lift blocks to categorize them by
weight), and are learnt in a self-supervised manner with approximately 2.5
times less data than conventional supervised planners. We show that these
behaviors can be re-purposed and fine-tuned (e.g., from lifting to pushing or
other downstream tasks). Finally, we show that the knowledge of causal factor
representations aids zero-shot learning for more complex tasks. Visit
https://sites.google.com/usc.edu/causal-curiosity/home for website. | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
Artificial Intelligence (AI), defined in its most simple form, is a
technological tool that makes machines intelligent. Since learning is at the
core of intelligence, machine learning poses itself as a core sub-field of AI.
Then there comes a subclass of machine learning, known as deep learning, to
address the limitations of their predecessors. AI has generally acquired its
prominence over the past few years due to its considerable progress in various
fields. AI has vastly invaded the realm of research. This has led physicists to
attentively direct their research towards implementing AI tools. Their central
aim has been to gain better understanding and enrich their intuition. This
review article is meant to supplement the previously presented efforts to
bridge the gap between AI and physics, and take a serious step forward to
filter out the "Babelian" clashes brought about from such gabs. This
necessitates first to have fundamental knowledge about common AI tools. To this
end, the review's primary focus shall be on deep learning models called
artificial neural networks. They are deep learning models which train
themselves through different learning processes. It discusses also the concept
of Markov decision processes. Finally, shortcut to the main goal, the review
thoroughly examines how these neural networks are capable to construct a
physical theory describing some observations without applying any previous
physical knowledge. | [
"cs.LG",
"cond-mat.dis-nn",
"physics.comp-ph",
"quant-ph",
"stat.ML"
] |
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