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We propose TabTransformer, a novel deep tabular data modeling architecture
for supervised and semi-supervised learning. The TabTransformer is built upon
self-attention based Transformers. The Transformer layers transform the
embeddings of categorical features into robust contextual embeddings to achieve
higher prediction accuracy. Through extensive experiments on fifteen publicly
available datasets, we show that the TabTransformer outperforms the
state-of-the-art deep learning methods for tabular data by at least 1.0% on
mean AUC, and matches the performance of tree-based ensemble models.
Furthermore, we demonstrate that the contextual embeddings learned from
TabTransformer are highly robust against both missing and noisy data features,
and provide better interpretability. Lastly, for the semi-supervised setting we
develop an unsupervised pre-training procedure to learn data-driven contextual
embeddings, resulting in an average 2.1% AUC lift over the state-of-the-art
methods. | [
"cs.LG",
"cs.AI"
] |
Partial domain adaptation aims to adapt knowledge from a larger and more
diverse source domain to a smaller target domain with less number of classes,
which has attracted appealing attention. Recent practice on domain adaptation
manages to extract effective features by incorporating the pseudo labels for
the target domain to better fight off the cross-domain distribution
divergences. However, it is essential to align target data with only a small
set of source data. In this paper, we develop a novel Discriminative
Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target
labels with a cross-domain graph in a weighted scheme. Specifically, a weighted
cross-domain center loss and weighted cross-domain graph propagation are
proposed to couple unlabeled target data to related source samples for
discriminative cross-domain feature learning, where irrelevant source centers
will be ignored, to alleviate the marginal and conditional disparities
simultaneously. Experimental evaluations on several popular benchmarks
demonstrate the effectiveness of our proposed approach on facilitating the
recognition for the unlabeled target domain, through comparing it to the
state-of-the-art partial domain adaptation approaches. | [
"cs.CV"
] |
What is the difference between goal-directed and habitual behavior? We
propose a novel computational framework of decision making with Bayesian
inference, in which everything is integrated as an entire neural network model.
The model learns to predict environmental state transitions by self-exploration
and generating motor actions by sampling stochastic internal states ${z}$.
Habitual behavior, which is obtained from the prior distribution of ${z}$, is
acquired by reinforcement learning. Goal-directed behavior is determined from
the posterior distribution of ${z}$ by planning, using active inference which
optimizes the past, current and future ${z}$ by minimizing the variational free
energy for the desired future observation constrained by the observed sensory
sequence. We demonstrate the effectiveness of the proposed framework by
experiments in a sensorimotor navigation task with camera observations and
continuous motor actions. | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
Event-based cameras are novel, efficient sensors inspired by the human vision
system, generating an asynchronous, pixel-wise stream of data. Learning from
such data is generally performed through heavy preprocessing and event
integration into images. This requires buffering of possibly long sequences and
can limit the response time of the inference system. In this work, we instead
propose to directly use events from a DVS camera, a stream of intensity changes
and their spatial coordinates. This sequence is used as the input for a novel
\emph{asynchronous} RNN-like architecture, the Input-filtering Neural ODEs
(INODE). This is inspired by the dynamical systems and filtering literature.
INODE is an extension of Neural ODEs (NODE) that allows for input signals to be
continuously fed to the network, like in filtering. The approach naturally
handles batches of time series with irregular time-stamps by implementing a
batch forward Euler solver. INODE is trained like a standard RNN, it learns to
discriminate short event sequences and to perform event-by-event online
inference. We demonstrate our approach on a series of classification tasks,
comparing against a set of LSTM baselines. We show that, independently of the
camera resolution, INODE can outperform the baselines by a large margin on the
ASL task and it's on par with a much larger LSTM for the NCALTECH task.
Finally, we show that INODE is accurate even when provided with very few
events. | [
"cs.CV",
"cs.NE"
] |
Understanding the internal representations of deep neural networks (DNNs) is
crucal to explain their behavior. The interpretation of individual units, which
are neurons in MLPs or convolution kernels in convolutional networks, has been
paid much attention given their fundamental role. However, recent research
(Morcos et al. 2018) presented a counterintuitive phenomenon, which suggests
that an individual unit with high class selectivity, called interpretable
units, has poor contributions to generalization of DNNs. In this work, we
provide a new perspective to understand this counterintuitive phenomenon, which
makes sense when we introduce Representative Substitution (RS). Instead of
individually selective units with classes, the RS refers to the independence of
a unit's representations in the same layer without any annotation. Our
experiments demonstrate that interpretable units have high RS which are not
critical to network's generalization. The RS provides new insights into the
interpretation of DNNs and suggests that we need to focus on the independence
and relationship of the representations. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Visual data in autonomous driving perception, such as camera image and LiDAR
point cloud, can be interpreted as a mixture of two aspects: semantic feature
and geometric structure. Semantics come from the appearance and context of
objects to the sensor, while geometric structure is the actual 3D shape of
point clouds. Most detectors on LiDAR point clouds focus only on analyzing the
geometric structure of objects in real 3D space. Unlike previous works, we
propose to learn both semantic feature and geometric structure via a unified
multi-view framework. Our method exploits the nature of LiDAR scans -- 2D range
images, and applies well-studied 2D convolutions to extract semantic features.
By fusing semantic and geometric features, our method outperforms
state-of-the-art approaches in all categories by a large margin. The
methodology of combining semantic and geometric features provides a unique
perspective of looking at the problems in real-world 3D point cloud detection. | [
"cs.CV"
] |
Issued from Optimal Transport, the Wasserstein distance has gained importance
in Machine Learning due to its appealing geometrical properties and the
increasing availability of efficient approximations. In this work, we consider
the problem of estimating the Wasserstein distance between two probability
distributions when observations are polluted by outliers. To that end, we
investigate how to leverage Medians of Means (MoM) estimators to robustify the
estimation of Wasserstein distance. Exploiting the dual Kantorovitch
formulation of Wasserstein distance, we introduce and discuss novel MoM-based
robust estimators whose consistency is studied under a data contamination model
and for which convergence rates are provided. These MoM estimators enable to
make Wasserstein Generative Adversarial Network (WGAN) robust to outliers, as
witnessed by an empirical study on two benchmarks CIFAR10 and Fashion MNIST.
Eventually, we discuss how to combine MoM with the entropy-regularized
approximation of the Wasserstein distance and propose a simple MoM-based
re-weighting scheme that could be used in conjunction with the Sinkhorn
algorithm. | [
"stat.ML",
"cs.LG"
] |
Many reinforcement learning (RL) environments consist of independent entities
that interact sparsely. In such environments, RL agents have only limited
influence over other entities in any particular situation. Our idea in this
work is that learning can be efficiently guided by knowing when and what the
agent can influence with its actions. To achieve this, we introduce a measure
of situation-dependent causal influence based on conditional mutual information
and show that it can reliably detect states of influence. We then propose
several ways to integrate this measure into RL algorithms to improve
exploration and off-policy learning. All modified algorithms show strong
increases in data efficiency on robotic manipulation tasks. | [
"cs.LG"
] |
Recent research on Software-Defined Networking (SDN) strongly promotes the
adoption of distributed controller architectures. To achieve high network
performance, designing a scheduling function (SF) to properly dispatch requests
from each switch to suitable controllers becomes critical. However, existing
literature tends to design the SF targeted at specific network settings. In
this paper, a reinforcement-learning-based (RL) approach is proposed with the
aim to automatically learn a general, effective, and efficient SF. In
particular, a new dispatching system is introduced in which the SF is
represented as a neural network that determines the priority of each
controller. Based on the priorities, a controller is selected using our
proposed probability selection scheme to balance the trade-off between
exploration and exploitation during learning. In order to train a general SF,
we first formulate the scheduling function design problem as an RL problem.
Then a new training approach is developed based on a state-of-the-art deep RL
algorithm. Our simulation results show that our RL approach can rapidly design
(or learn) SFs with optimal performance. Apart from that, the trained SF can
generalize well and outperforms commonly used scheduling heuristics under
various network settings. | [
"cs.LG",
"cs.NI",
"stat.ML"
] |
The ability to decompose scenes in terms of abstract building blocks is
crucial for general intelligence. Where those basic building blocks share
meaningful properties, interactions and other regularities across scenes, such
decompositions can simplify reasoning and facilitate imagination of novel
scenarios. In particular, representing perceptual observations in terms of
entities should improve data efficiency and transfer performance on a wide
range of tasks. Thus we need models capable of discovering useful
decompositions of scenes by identifying units with such regularities and
representing them in a common format. To address this problem, we have
developed the Multi-Object Network (MONet). In this model, a VAE is trained
end-to-end together with a recurrent attention network -- in a purely
unsupervised manner -- to provide attention masks around, and reconstructions
of, regions of images. We show that this model is capable of learning to
decompose and represent challenging 3D scenes into semantically meaningful
components, such as objects and background elements. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
A tensor provides a concise way to codify the interdependence of complex
data. Treating a tensor as a d-way array, each entry records the interaction
between the different indices. Clustering provides a way to parse the
complexity of the data into more readily understandable information. Clustering
methods are heavily dependent on the algorithm of choice, as well as the chosen
hyperparameters of the algorithm. However, their sensitivity to data scales is
largely unknown.
In this work, we apply the discrete wavelet transform to analyze the effects
of coarse-graining on clustering tensor data. We are particularly interested in
understanding how scale effects clustering of the Earth's climate system. The
discrete wavelet transform allows classification of the Earth's climate across
a multitude of spatial-temporal scales. The discrete wavelet transform is used
to produce an ensemble of classification estimates, as opposed to a single
classification. Information theoretic approaches are used to identify important
scale lenghts in clustering The L15 Climate Datset. We also discover a
sub-collection of the ensemble that spans the majority of the variance
observed, allowing for efficient consensus clustering techniques that can be
used to identify climate biomes. | [
"cs.LG",
"stat.ML"
] |
Teaching plays a very important role in our society, by spreading human
knowledge and educating our next generations. A good teacher will select
appropriate teaching materials, impact suitable methodologies, and set up
targeted examinations, according to the learning behaviors of the students. In
the field of artificial intelligence, however, one has not fully explored the
role of teaching, and pays most attention to machine \emph{learning}. In this
paper, we argue that equal attention, if not more, should be paid to teaching,
and furthermore, an optimization framework (instead of heuristics) should be
used to obtain good teaching strategies. We call this approach `learning to
teach'. In the approach, two intelligent agents interact with each other: a
student model (which corresponds to the learner in traditional machine learning
algorithms), and a teacher model (which determines the appropriate data, loss
function, and hypothesis space to facilitate the training of the student
model). The teacher model leverages the feedback from the student model to
optimize its own teaching strategies by means of reinforcement learning, so as
to achieve teacher-student co-evolution. To demonstrate the practical value of
our proposed approach, we take the training of deep neural networks (DNN) as an
example, and show that by using the learning to teach techniques, we are able
to use much less training data and fewer iterations to achieve almost the same
accuracy for different kinds of DNN models (e.g., multi-layer perceptron,
convolutional neural networks and recurrent neural networks) under various
machine learning tasks (e.g., image classification and text understanding). | [
"cs.LG",
"cs.AI"
] |
Image generation has raised tremendous attention in both academic and
industrial areas, especially for the conditional and target-oriented image
generation, such as criminal portrait and fashion design. Although the current
studies have achieved preliminary results along this direction, they always
focus on class labels as the condition where spatial contents are randomly
generated from latent vectors. Edge details are usually blurred since spatial
information is difficult to preserve. In light of this, we propose a novel
Spatially Constrained Generative Adversarial Network (SCGAN), which decouples
the spatial constraints from the latent vector and makes these constraints
feasible as additional controllable signals. To enhance the spatial
controllability, a generator network is specially designed to take a semantic
segmentation, a latent vector and an attribute-level label as inputs step by
step. Besides, a segmentor network is constructed to impose spatial constraints
on the generator. Experimentally, we provide both visual and quantitative
results on CelebA and DeepFashion datasets, and demonstrate that the proposed
SCGAN is very effective in controlling the spatial contents as well as
generating high-quality images. | [
"cs.CV"
] |
In this paper we introduce evidence transfer for clustering, a deep learning
method that can incrementally manipulate the latent representations of an
autoencoder, according to external categorical evidence, in order to improve a
clustering outcome. By evidence transfer we define the process by which the
categorical outcome of an external, auxiliary task is exploited to improve a
primary task, in this case representation learning for clustering. Our proposed
method makes no assumptions regarding the categorical evidence presented, nor
the structure of the latent space. We compare our method, against the baseline
solution by performing k-means clustering before and after its deployment.
Experiments with three different kinds of evidence show that our method
effectively manipulates the latent representations when introduced with real
corresponding evidence, while remaining robust when presented with low quality
evidence. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot. | [
"cs.CV",
"cs.DB",
"cs.LG",
"cs.RO"
] |
Distance Metric Learning (DML) has drawn much attention over the last two
decades. A number of previous works have shown that it performs well in
measuring the similarities of individuals given a set of correctly labeled
pairwise data by domain experts. These important and precisely-labeled pairwise
data are often highly sensitive in real world (e.g., patients similarity). This
paper studies, for the first time, how pairwise information can be leaked to
attackers during distance metric learning, and develops differential pairwise
privacy (DPP), generalizing the definition of standard differential privacy,
for secure metric learning. Unlike traditional differential privacy which only
applies to independent samples, thus cannot be used for pairwise data, DPP
successfully deals with this problem by reformulating the worst case.
Specifically, given the pairwise data, we reveal all the involved correlations
among pairs in the constructed undirected graph. DPP is then formalized that
defines what kind of DML algorithm is private to preserve pairwise data. After
that, a case study employing the contrastive loss is exhibited to clarify the
details of implementing a DPP-DML algorithm. Particularly, the sensitivity
reduction technique is proposed to enhance the utility of the output distance
metric. Experiments both on a toy dataset and benchmarks demonstrate that the
proposed scheme achieves pairwise data privacy without compromising the output
performance much (Accuracy declines less than 0.01 throughout all benchmark
datasets when the privacy budget is set at 4). | [
"cs.LG",
"stat.ML"
] |
Semantically-aligned $(speech, image)$ datasets can be used to explore
"visually-grounded speech". In a majority of existing investigations, features
of an image signal are extracted using neural networks "pre-trained" on other
tasks (e.g., classification on ImageNet). In still others, pre-trained networks
are used to extract audio features prior to semantic embedding. Without
"transfer learning" through pre-trained initialization or pre-trained feature
extraction, previous results have tended to show low rates of recall in $speech
\rightarrow image$ and $image \rightarrow speech$ queries.
Choosing appropriate neural architectures for encoders in the speech and
image branches and using large datasets, one can obtain competitive recall
rates without any reliance on any pre-trained initialization or feature
extraction: $(speech,image)$ semantic alignment and $speech \rightarrow image$
and $image \rightarrow speech$ retrieval are canonical tasks worthy of
independent investigation of their own and allow one to explore other
questions---e.g., the size of the audio embedder can be reduced significantly
with little loss of recall rates in $speech \rightarrow image$ and $image
\rightarrow speech$ queries. | [
"cs.LG",
"cs.CV",
"cs.IT",
"cs.MM",
"math.IT",
"68T01, 68T05, 68T07, 68T10, 62P15",
"I.2; I.2.0; I.2.6; I.2.7; I.2.11; I.5; I.5.1; I.5.2; I.5.4; I.4.10;\n H.5.1; H.5.2; H.3.3"
] |
Objective: Modern medicine needs to shift from a wait and react, curative
discipline to a preventative, interdisciplinary science aiming at providing
personalised, systemic and precise treatment plans to patients. The aim of this
work is to present how the integration of machine learning approaches with
mechanistic computational modelling could yield a reliable infrastructure to
run probabilistic simulations where the entire organism is considered as a
whole. Methods: We propose a general framework that composes advanced AI
approaches and integrates mathematical modelling in order to provide a
panoramic view over current and future physiological conditions. The proposed
architecture is based on a graph neural network (GNNs) forecasting clinically
relevant endpoints (such as blood pressure) and a generative adversarial
network (GANs) providing a proof of concept of transcriptomic integrability.
Results: We show the results of the investigation of pathological effects of
overexpression of ACE2 across different signalling pathways in multiple tissues
on cardiovascular functions. We provide a proof of concept of integrating a
large set of composable clinical models using molecular data to drive local and
global clinical parameters and derive future trajectories representing the
evolution of the physiological state of the patient. Significance: We argue
that the graph representation of a computational patient has potential to solve
important technological challenges in integrating multiscale computational
modelling with AI. We believe that this work represents a step forward towards
a healthcare digital twin. | [
"stat.ML",
"cs.LG"
] |
We present a new deep learning approach for real-time 3D human action
recognition from skeletal data and apply it to develop a vision-based
intelligent surveillance system. Given a skeleton sequence, we propose to
encode skeleton poses and their motions into a single RGB image. An Adaptive
Histogram Equalization (AHE) algorithm is then applied on the color images to
enhance their local patterns and generate more discriminative features. For
learning and classification tasks, we design Deep Neural Networks based on the
Densely Connected Convolutional Architecture (DenseNet) to extract features
from enhanced-color images and classify them into classes. Experimental results
on two challenging datasets show that the proposed method reaches
state-of-the-art accuracy, whilst requiring low computational time for training
and inference. This paper also introduces CEMEST, a new RGB-D dataset depicting
passenger behaviors in public transport. It consists of 203 untrimmed
real-world surveillance videos of realistic normal and anomalous events. We
achieve promising results on real conditions of this dataset with the support
of data augmentation and transfer learning techniques. This enables the
construction of real-world applications based on deep learning for enhancing
monitoring and security in public transport. | [
"cs.CV"
] |
Statistical learning theory provides the foundation to applied machine
learning, and its various successful applications in computer vision, natural
language processing and other scientific domains. The theory, however, does not
take into account the unique challenges of performing statistical learning in
geospatial settings. For instance, it is well known that model errors cannot be
assumed to be independent and identically distributed in geospatial (a.k.a.
regionalized) variables due to spatial correlation; and trends caused by
geophysical processes lead to covariate shifts between the domain where the
model was trained and the domain where it will be applied, which in turn harm
the use of classical learning methodologies that rely on random samples of the
data. In this work, we introduce the geostatistical (transfer) learning
problem, and illustrate the challenges of learning from geospatial data by
assessing widely-used methods for estimating generalization error of learning
models, under covariate shift and spatial correlation. Experiments with
synthetic Gaussian process data as well as with real data from geophysical
surveys in New Zealand indicate that none of the methods are adequate for model
selection in a geospatial context. We provide general guidelines regarding the
choice of these methods in practice while new methods are being actively
researched. | [
"stat.ML",
"cs.LG"
] |
In most recent years, deep convolutional neural networks (DCNNs) based image
super-resolution (SR) has gained increasing attention in multimedia and
computer vision communities, focusing on restoring the high-resolution (HR)
image from a low-resolution (LR) image. However, one nonnegligible flaw of
DCNNs based methods is that most of them are not able to restore
high-resolution images containing sufficient high-frequency information from
low-resolution images with low-frequency information redundancy. Worse still,
as the depth of DCNNs increases, the training easily encounters the problem of
vanishing gradients, which makes the training more difficult. These problems
hinder the effectiveness of DCNNs in image SR task. To solve these problems, we
propose the Multi-view Aware Attention Networks (MAANet) for image SR task.
Specifically, we propose the local aware (LA) and global aware (GA) attention
to deal with LR features in unequal manners, which can highlight the
high-frequency components and discriminate each feature from LR images in the
local and the global views, respectively. Furthermore, we propose the local
attentive residual-dense (LARD) block, which combines the LA attention with
multiple residual and dense connections, to fit a deeper yet easy to train
architecture. The experimental results show that our proposed approach can
achieve remarkable performance compared with other state-of-the-art methods. | [
"cs.CV"
] |
Deep learning is a rapidly developing approach in the field of infrared and
visible image fusion. In this context, the use of dense blocks in deep networks
significantly improves the utilization of shallow information, and the
combination of the Generative Adversarial Network (GAN) also improves the
fusion performance of two source images. We propose a new method based on dense
blocks and GANs , and we directly insert the input image-visible light image in
each layer of the entire network. We use SSIM and gradient loss functions that
are more consistent with perception instead of mean square error loss. After
the adversarial training between the generator and the discriminator, we show
that a trained end-to-end fusion network -- the generator network -- is finally
obtained. Our experiments show that the fused images obtained by our approach
achieve good score based on multiple evaluation indicators. Further, our fused
images have better visual effects in multiple sets of contrasts, which are more
satisfying to human visual perception. | [
"cs.CV"
] |
Neural Network is a powerful Machine Learning tool that shows outstanding
performance in Computer Vision, Natural Language Processing, and Artificial
Intelligence. In particular, recently proposed ResNet architecture and its
modifications produce state-of-the-art results in image classification
problems. ResNet and most of the previously proposed architectures have a fixed
structure and apply the same transformation to all input images. In this work,
we develop a ResNet-based model that dynamically selects Computational Units
(CU) for each input object from a learned set of transformations. Dynamic
selection allows the network to learn a sequence of useful transformations and
apply only required units to predict the image label. We compare our model to
ResNet-38 architecture and achieve better results than the original ResNet on
CIFAR-10.1 test set. While examining the produced paths, we discovered that the
network learned different routes for images from different classes and similar
routes for similar images. | [
"stat.ML",
"cs.LG"
] |
Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by
providing temporal scopes (start and end times) on each edge in the KG. While
Question Answering over KG (KGQA) has received some attention from the research
community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored
area. Lack of broad coverage datasets has been another factor limiting progress
in this area. We address this challenge by presenting CRONQUESTIONS, the
largest known Temporal KGQA dataset, clearly stratified into buckets of
structural complexity. CRONQUESTIONS expands the only known previous dataset by
a factor of 340x. We find that various state-of-the-art KGQA methods fall far
short of the desired performance on this new dataset. In response, we also
propose CRONKGQA, a transformer-based solution that exploits recent advances in
Temporal KG embeddings, and achieves performance superior to all baselines,
with an increase of 120% in accuracy over the next best performing method.
Through extensive experiments, we give detailed insights into the workings of
CRONKGQA, as well as situations where significant further improvements appear
possible. In addition to the dataset, we have released our code as well. | [
"cs.LG"
] |
This paper tackles the problem of real-time semantic segmentation of high
definition videos using a hybrid GPU / CPU approach. We propose an Efficient
Video Segmentation(EVS) pipeline that combines:
(i) On the CPU, a very fast optical flow method, that is used to exploit the
temporal aspect of the video and propagate semantic information from one frame
to the next. It runs in parallel with the GPU.
(ii) On the GPU, two Convolutional Neural Networks: A main segmentation
network that is used to predict dense semantic labels from scratch, and a
Refiner that is designed to improve predictions from previous frames with the
help of a fast Inconsistencies Attention Module (IAM). The latter can identify
regions that cannot be propagated accurately.
We suggest several operating points depending on the desired frame rate and
accuracy. Our pipeline achieves accuracy levels competitive to the existing
real-time methods for semantic image segmentation(mIoU above 60%), while
achieving much higher frame rates. On the popular Cityscapes dataset with high
resolution frames (2048 x 1024), the proposed operating points range from 80 to
1000 Hz on a single GPU and CPU. | [
"cs.CV"
] |
Training high-accuracy object detection models requires large and diverse
annotated datasets. However, creating these data-sets is time-consuming and
expensive since it relies on human annotators. We design, implement, and
evaluate TagMe, a new approach for automatic object annotation in videos that
uses GPS data. When the GPS trace of an object is available, TagMe matches the
object's motion from GPS trace and the pixels' motions in the video to find the
pixels belonging to the object in the video and creates the bounding box
annotations of the object. TagMe works using passive data collection and can
continuously generate new object annotations from outdoor video streams without
any human annotators. We evaluate TagMe on a dataset of 100 video clips. We
show TagMe can produce high-quality object annotations in a fully-automatic and
low-cost way. Compared with the traditional human-in-the-loop solution, TagMe
can produce the same amount of annotations at a much lower cost, e.g., up to
110x. | [
"cs.CV",
"cs.LG"
] |
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease
which may result in loss of vision. There is limited access to high-quality
relevant retinal images and poor understanding of the features defining
sub-classes of this disease. Motivated by recent advances in machine learning
we specifically explore the potential of generative modeling, using Generative
Adversarial Networks (GANs) and style transferring, to facilitate clinical
diagnosis and disease understanding by feature extraction. We design an
analytic pipeline which first generates synthetic retinal images from clinical
images; a subsequent verification step is applied. In the synthesizing step we
merge GANs (DCGANs and WGANs architectures) and style transferring for the
image generation, whereas the verified step controls the accuracy of the
generated images. We find that the generated images contain sufficient
pathological details to facilitate ophthalmologists' task of disease
classification and in discovery of disease relevant features. In particular,
our system predicts the drusen and geographic atrophy sub-classes of AMD.
Furthermore, the performance using CFP images for GANs outperforms the
classification based on using only the original clinical dataset. Our results
are evaluated using existing classifier of retinal diseases and class activated
maps, supporting the predictive power of the synthetic images and their utility
for feature extraction. Our code examples are available online. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
With the huge success of deep learning, other machine learning paradigms have
had to take back seat. Yet other models, particularly rule-based, are more
readable and explainable and can even be competitive when labelled data is not
abundant. However, most of the existing rule-based classifiers suffer from the
production of a large number of classification rules, affecting the model
readability. This hampers the classification accuracy as noisy rules might not
add any useful informationfor classification and also lead to longer
classification time. In this study, we propose SigD2 which uses a novel,
two-stage pruning strategy which prunes most of the noisy, redundant and
uninteresting rules and makes the classification model more accurate and
readable. To make SigDirect more competitive with the most prevalent but
uninterpretable machine learning-based classifiers like neural networks and
support vector machines, we propose bagging and boosting on the ensemble of the
SigDirect classifier. The results of the proposed algorithms are quite
promising and we are able to obtain a minimal set of statistically significant
rules for classification without jeopardizing the classification accuracy. We
use 15 UCI datasets and compare our approach with eight existing systems.The
SigD2 and boosted SigDirect (ACboost) ensemble model outperform various
state-of-the-art classifiers not only in terms of classification accuracy but
also in terms of the number of rules. | [
"cs.LG",
"stat.ML"
] |
Transformers are being used extensively across several sequence modeling
tasks. Significant research effort has been devoted to experimentally probe the
inner workings of Transformers. However, our conceptual and theoretical
understanding of their power and inherent limitations is still nascent. In
particular, the roles of various components in Transformers such as positional
encodings, attention heads, residual connections, and feedforward networks, are
not clear. In this paper, we take a step towards answering these questions. We
analyze the computational power as captured by Turing-completeness. We first
provide an alternate and simpler proof to show that vanilla Transformers are
Turing-complete and then we prove that Transformers with only positional
masking and without any positional encoding are also Turing-complete. We
further analyze the necessity of each component for the Turing-completeness of
the network; interestingly, we find that a particular type of residual
connection is necessary. We demonstrate the practical implications of our
results via experiments on machine translation and synthetic tasks. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Representation learning, especially which by using deep learning, has been
widely applied in classification. However, how to use limited size of labeled
data to achieve good classification performance with deep neural network, and
how can the learned features further improve classification remain indefinite.
In this paper, we propose Horizontal Voting Vertical Voting and Horizontal
Stacked Ensemble methods to improve the classification performance of deep
neural networks. In the ICML 2013 Black Box Challenge, via using these methods
independently, Bing Xu achieved 3rd in public leaderboard, and 7th in private
leaderboard; Jingjing Xie achieved 4th in public leaderboard, and 5th in
private leaderboard. | [
"cs.LG",
"stat.ML"
] |
Data inconsistency and bias are inevitable among different facial expression
recognition (FER) datasets due to subjective annotating process and different
collecting conditions. Recent works resort to adversarial mechanisms that learn
domain-invariant features to mitigate domain shift. However, most of these
works focus on holistic feature adaptation, and they ignore local features that
are more transferable across different datasets. Moreover, local features carry
more detailed and discriminative content for expression recognition, and thus
integrating local features may enable fine-grained adaptation. In this work, we
propose a novel Adversarial Graph Representation Adaptation (AGRA) framework
that unifies graph representation propagation with adversarial learning for
cross-domain holistic-local feature co-adaptation. To achieve this, we first
build a graph to correlate holistic and local regions within each domain and
another graph to correlate these regions across different domains. Then, we
learn the per-class statistical distribution of each domain and extract
holistic-local features from the input image to initialize the corresponding
graph nodes. Finally, we introduce two stacked graph convolution networks to
propagate holistic-local feature within each domain to explore their
interaction and across different domains for holistic-local feature
co-adaptation. In this way, the AGRA framework can adaptively learn
fine-grained domain-invariant features and thus facilitate cross-domain
expression recognition. We conduct extensive and fair experiments on several
popular benchmarks and show that the proposed AGRA framework achieves superior
performance over previous state-of-the-art methods. | [
"cs.CV"
] |
We start with a brief introduction to reinforcement learning (RL), about its
successful stories, basics, an example, issues, the ICML 2019 Workshop on RL
for Real Life, how to use it, study material and an outlook. Then we discuss a
selection of RL applications, including recommender systems, computer systems,
energy, finance, healthcare, robotics, and transportation. | [
"cs.LG",
"cs.AI"
] |
Deep neural networks have gained tremendous importance in many computer
vision tasks. However, their power comes at the cost of large amounts of
annotated data required for supervised training. In this work we review and
compare different techniques available in the literature to improve training
results without acquiring additional annotated real-world data. This goal is
mostly achieved by applying annotation-preserving transformations to existing
data or by synthetically creating more data. | [
"cs.CV",
"cs.LG"
] |
Advances in deep learning recognition have led to accurate object detection
with 2D images. However, these 2D perception methods are insufficient for
complete 3D world information. Concurrently, advanced 3D shape estimation
approaches focus on the shape itself, without considering metric scale. These
methods cannot determine the accurate location and orientation of objects. To
tackle this problem, we propose a framework that jointly estimates a metric
scale shape and pose from a single RGB image. Our framework has two branches:
the Metric Scale Object Shape branch (MSOS) and the Normalized Object
Coordinate Space branch (NOCS). The MSOS branch estimates the metric scale
shape observed in the camera coordinates. The NOCS branch predicts the
normalized object coordinate space (NOCS) map and performs similarity
transformation with the rendered depth map from a predicted metric scale mesh
to obtain 6d pose and size. Additionally, we introduce the Normalized Object
Center Estimation (NOCE) to estimate the geometrically aligned distance from
the camera to the object center. We validated our method on both synthetic and
real-world datasets to evaluate category-level object pose and shape. | [
"cs.CV",
"cs.RO"
] |
We propose a method to detect and reconstruct multiple 3D objects from a
single RGB image. The key idea is to optimize for detection, alignment and
shape jointly over all objects in the RGB image, while focusing on realistic
and physically plausible reconstructions. To this end, we propose a keypoint
detector that localizes objects as center points and directly predicts all
object properties, including 9-DoF bounding boxes and 3D shapes -- all in a
single forward pass. The proposed method formulates 3D shape reconstruction as
a shape selection problem, i.e. it selects among exemplar shapes from a given
database. This makes it agnostic to shape representations, which enables a
lightweight reconstruction of realistic and visually-pleasing shapes based on
CAD-models, while the training objective is formulated around point clouds and
voxel representations. A collision-loss promotes non-intersecting objects,
further increasing the reconstruction realism. Given the RGB image, the
presented approach performs lightweight reconstruction in a single-stage, it is
real-time capable, fully differentiable and end-to-end trainable. Our
experiments compare multiple approaches for 9-DoF bounding box estimation,
evaluate the novel shape-selection mechanism and compare to recent methods in
terms of 3D bounding box estimation and 3D shape reconstruction quality. | [
"cs.CV"
] |
Dimensionality reduction methods for count data are critical to a wide range
of applications in medical informatics and other fields where model
interpretability is paramount. For such data, hierarchical Poisson matrix
factorization (HPF) and other sparse probabilistic non-negative matrix
factorization (NMF) methods are considered to be interpretable generative
models. They consist of sparse transformations for decoding their learned
representations into predictions. However, sparsity in representation decoding
does not necessarily imply sparsity in the encoding of representations from the
original data features. HPF is often incorrectly interpreted in the literature
as if it possesses encoder sparsity. The distinction between decoder sparsity
and encoder sparsity is subtle but important. Due to the lack of encoder
sparsity, HPF does not possess the column-clustering property of classical NMF
-- the factor loading matrix does not sufficiently define how each factor is
formed from the original features. We address this deficiency by
self-consistently enforcing encoder sparsity, using a generalized additive
model (GAM), thereby allowing one to relate each representation coordinate to a
subset of the original data features. In doing so, the method also gains the
ability to perform feature selection. We demonstrate our method on simulated
data and give an example of how encoder sparsity is of practical use in a
concrete application of representing inpatient comorbidities in Medicare
patients. | [
"cs.LG",
"q-bio.QM",
"stat.ML"
] |
Batch Normalization (BN) is extensively employed in various network
architectures by performing standardization within mini-batches.
A full understanding of the process has been a central target in the deep
learning communities.
Unlike existing works, which usually only analyze the standardization
operation, this paper investigates the more general Batch Whitening (BW). Our
work originates from the observation that while various whitening
transformations equivalently improve the conditioning, they show significantly
different behaviors in discriminative scenarios and training Generative
Adversarial Networks (GANs).
We attribute this phenomenon to the stochasticity that BW introduces.
We quantitatively investigate the stochasticity of different whitening
transformations and show that it correlates well with the optimization
behaviors during training.
We also investigate how stochasticity relates to the estimation of population
statistics during inference.
Based on our analysis, we provide a framework for designing and comparing BW
algorithms in different scenarios.
Our proposed BW algorithm improves the residual networks by a significant
margin on ImageNet classification.
Besides, we show that the stochasticity of BW can improve the GAN's
performance with, however, the sacrifice of the training stability. | [
"cs.CV",
"cs.LG"
] |
In this paper, we present a large-scale Diverse Real-world image
Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer
Super-Resolution (SR) network, exploring the utility of guiding SR model with
low-level image components. DRealSR establishes a new SR benchmark with diverse
real-world degradation processes, mitigating the limitations of conventional
simulated image degradation. In general, the targets of SR vary with image
regions with different low-level image components, e.g., smoothness preserving
for flat regions, sharpening for edges, and detail enhancing for textures.
Learning an SR model with conventional pixel-wise loss usually is easily
dominated by flat regions and edges, and fails to infer realistic details of
complex textures. We propose a Component Divide-and-Conquer (CDC) model and a
Gradient-Weighted (GW) loss for SR. Our CDC parses an image with three
components, employs three Component-Attentive Blocks (CABs) to learn attentive
masks and intermediate SR predictions with an intermediate supervision learning
strategy, and trains an SR model following a divide-and-conquer learning
principle. Our GW loss also provides a feasible way to balance the difficulties
of image components for SR. Extensive experiments validate the superior
performance of our CDC and the challenging aspects of our DRealSR dataset
related to diverse real-world scenarios. Our dataset and codes are publicly
available at
https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution | [
"cs.CV"
] |
Multi-task learning (MTL) is a supervised learning paradigm in which the
prediction models for several related tasks are learned jointly to achieve
better generalization performance. When there are only a few training examples
per task, MTL considerably outperforms the traditional Single task learning
(STL) in terms of prediction accuracy. In this work we develop an MTL based
approach for classifying documents that are archived within dual concept
hierarchies, namely, DMOZ and Wikipedia. We solve the multi-class
classification problem by defining one-versus-rest binary classification tasks
for each of the different classes across the two hierarchical datasets. Instead
of learning a linear discriminant for each of the different tasks
independently, we use a MTL approach with relationships between the different
tasks across the datasets established using the non-parametric, lazy, nearest
neighbor approach. We also develop and evaluate a transfer learning (TL)
approach and compare the MTL (and TL) methods against the standard single task
learning and semi-supervised learning approaches. Our empirical results
demonstrate the strength of our developed methods that show an improvement
especially when there are fewer number of training examples per classification
task. | [
"cs.LG",
"stat.ML"
] |
In this paper we propose a data augmentation method for time series with
irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN).
Our approach is based on Conditional Generative Adversarial Networks (CGAN),
where the generative step is implemented by a deconvolutional NN and the
discriminative step by a convolutional NN. Both the generator and the
discriminator are conditioned on the sampling timestamps, to learn the hidden
relationship between data and timestamps, and consequently to generate new time
series. We evaluate our model with synthetic and real-world datasets. For the
synthetic data, we compare the performance of a classifier trained with
T-CGAN-generated data, against the performance of the same classifier trained
on the original data. Results show that classifiers trained on T-CGAN-generated
data perform the same as classifiers trained on real data, even with very short
time series and small training sets. For the real world datasets, we compare
our method with other techniques of data augmentation for time series, such as
time slicing and time warping, over a classification problem with unbalanced
datasets. Results show that our method always outperforms the other approaches,
both in case of regularly sampled and irregularly sampled time series. We
achieve particularly good performance in case with a small training set and
short, noisy, irregularly-sampled time series. | [
"cs.LG",
"stat.ML"
] |
As more machine learning agents interact with humans, it is increasingly a
prospect that an agent trained to perform a task optimally, using only a
measure of task performance as feedback, can violate societal norms for
acceptable behavior or cause harm. Value alignment is a property of intelligent
agents wherein they solely pursue non-harmful behaviors or human-beneficial
goals. We introduce an approach to value-aligned reinforcement learning, in
which we train an agent with two reward signals: a standard task performance
reward, plus a normative behavior reward. The normative behavior reward is
derived from a value-aligned prior model previously shown to classify text as
normative or non-normative. We show how variations on a policy shaping
technique can balance these two sources of reward and produce policies that are
both effective and perceived as being more normative. We test our
value-alignment technique on three interactive text-based worlds; each world is
designed specifically to challenge agents with a task as well as provide
opportunities to deviate from the task to engage in normative and/or altruistic
behavior. | [
"cs.LG",
"cs.AI",
"cs.HC"
] |
Event cameras are paradigm-shifting novel sensors that report asynchronous,
per-pixel brightness changes called 'events' with unparalleled low latency.
This makes them ideal for high speed, high dynamic range scenes where
conventional cameras would fail. Recent work has demonstrated impressive
results using Convolutional Neural Networks (CNNs) for video reconstruction and
optic flow with events. We present strategies for improving training data for
event based CNNs that result in 20-40% boost in performance of existing
state-of-the-art (SOTA) video reconstruction networks retrained with our
method, and up to 15% for optic flow networks. A challenge in evaluating event
based video reconstruction is lack of quality ground truth images in existing
datasets. To address this, we present a new High Quality Frames (HQF) dataset,
containing events and ground truth frames from a DAVIS240C that are
well-exposed and minimally motion-blurred. We evaluate our method on HQF +
several existing major event camera datasets. | [
"cs.CV"
] |
We propose Information Theoretic-Learning (ITL) divergence measures for
variational regularization of neural networks. We also explore ITL-regularized
autoencoders as an alternative to variational autoencoding bayes, adversarial
autoencoders and generative adversarial networks for randomly generating sample
data without explicitly defining a partition function. This paper also
formalizes, generative moment matching networks under the ITL framework. | [
"cs.LG"
] |
Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led
to certifiably optimal non-minimal solvers for several robotics and computer
vision problems. However, most non-minimal solvers rely on least-squares
formulations, and, as a result, are brittle against outliers. While a standard
approach to regain robustness against outliers is to use robust cost functions,
the latter typically introduce other non-convexities, preventing the use of
existing non-minimal solvers. In this paper, we enable the simultaneous use of
non-minimal solvers and robust estimation by providing a general-purpose
approach for robust global estimation, which can be applied to any problem
where a non-minimal solver is available for the outlier-free case. To this end,
we leverage the Black-Rangarajan duality between robust estimation and outlier
processes (which has been traditionally applied to early vision problems), and
show that graduated non-convexity (GNC) can be used in conjunction with
non-minimal solvers to compute robust solutions, without requiring an initial
guess. Although GNC's global optimality cannot be guaranteed, we demonstrate
the empirical robustness of the resulting robust non-minimal solvers in
applications, including point cloud and mesh registration, pose graph
optimization, and image-based object pose estimation (also called shape
alignment). Our solvers are robust to 70-80% of outliers, outperform RANSAC,
are more accurate than specialized local solvers, and faster than specialized
global solvers. We also propose the first certifiably optimal non-minimal
solver for shape alignment using SOS relaxation. | [
"cs.CV",
"cs.RO",
"math.OC",
"68T40, 74Pxx, 46N10, 65D19",
"I.2.9; G.1.6; I.4.5; I.2.10"
] |
Controllable painting generation plays a pivotal role in image stylization.
Currently, the control way of style transfer is subject to exemplar-based
reference or a random one-hot vector guidance. Few works focus on decoupling
the intrinsic properties of painting as control conditions, e.g., artist, genre
and period. Under this circumstance, we propose a novel framework adopting
multiple attributes from the painting to control the stylized results. An
asymmetrical cycle structure is equipped to preserve the fidelity, associating
with style preserving and attribute regression loss to keep the unique
distinction of colors and textures between domains. Several qualitative and
quantitative results demonstrate the effect of the combinations of multiple
attributes and achieve satisfactory performance. | [
"cs.CV"
] |
Document image enhancement and binarization methods are often used to improve
the accuracy and efficiency of document image analysis tasks such as text
recognition. Traditional non-machine-learning methods are constructed on
low-level features in an unsupervised manner but have difficulty with
binarization on documents with severely degraded backgrounds. Convolutional
neural network-based methods focus only on grayscale images and on local
textual features. In this paper, we propose a two-stage color document image
enhancement and binarization method using generative adversarial neural
networks. In the first stage, four color-independent adversarial networks are
trained to extract color foreground information from an input image for
document image enhancement. In the second stage, two independent adversarial
networks with global and local features are trained for image binarization of
documents of variable size. For the adversarial neural networks, we formulate
loss functions between a discriminator and generators having an encoder-decoder
structure. Experimental results show that the proposed method achieves better
performance than many classical and state-of-the-art algorithms over the
Document Image Binarization Contest (DIBCO) datasets, the LRDE Document
Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan
to release the shipping label dataset as well as our implementation code at
github.com/opensuh/DocumentBinarization/. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Rigid registration of multi-view and multi-platform LiDAR scans is a
fundamental problem in 3D mapping, robotic navigation, and large-scale urban
modeling applications. Data acquisition with LiDAR sensors involves scanning
multiple areas from different points of view, thus generating partially
overlapping point clouds of the real world scenes. Traditionally, ICP
(Iterative Closest Point) algorithm is used to register the acquired point
clouds together to form a unique point cloud that captures the scanned real
world scene. Conventional ICP faces local minima issues and often needs a
coarse initial alignment to converge to the optimum. In this work, we present
an algorithm for registering multiple, overlapping LiDAR scans. We introduce a
geometric metric called Transformation Compatibility Measure (TCM) which aids
in choosing the most similar point clouds for registration in each iteration of
the algorithm. The LiDAR scan most similar to the reference LiDAR scan is then
transformed using simplex technique. An optimization of the transformation
using gradient descent and simulated annealing techniques are then applied to
improve the resulting registration. We evaluate the proposed algorithm on four
different real world scenes and experimental results shows that the
registration performance of the proposed method is comparable or superior to
the traditionally used registration methods. Further, the algorithm achieves
superior registration results even when dealing with outliers. | [
"cs.CV",
"cs.GR",
"68U05",
"I.3.5; I.4.8; I.5.3"
] |
Efficient and easy segmentation of images and volumes is of great practical
importance. Segmentation problems that motivate our approach originate from
microscopy imaging commonly used in materials science, medicine, and biology.
We formulate image segmentation as a probabilistic pixel classification
problem, and we apply segmentation as a step towards characterising image
content. Our method allows the user to define structures of interest by
interactively marking a subset of pixels. Thanks to the real-time feedback, the
user can place new markings strategically, depending on the current outcome.
The final pixel classification may be obtained from a very modest user input.
An important ingredient of our method is a graph that encodes image content.
This graph is built in an unsupervised manner during initialisation and is
based on clustering of image features. Since we combine a limited amount of
user-labelled data with the clustering information obtained from the unlabelled
parts of the image, our method fits in the general framework of semi-supervised
learning. We demonstrate how this can be a very efficient approach to
segmentation through pixel classification. | [
"cs.CV"
] |
Massive biometric deployments are pervasive in today's world. But despite the
high accuracy of biometric systems, their computational efficiency degrades
drastically with an increase in the database size. Thus, it is essential to
index them. An ideal indexing scheme needs to generate codes that preserve the
intra-subject similarity as well as inter-subject dissimilarity. Here, in this
paper, we propose an iris indexing scheme using real-valued deep iris features
binarized to iris bar codes (IBC) compatible with the indexing structure.
Firstly, for extracting robust iris features, we have designed a network
utilizing the domain knowledge of ordinal filtering and learning their
nonlinear combinations. Later these real-valued features are binarized.
Finally, for indexing the iris dataset, we have proposed a loss that can
transform the binary feature into an improved feature compatible with the
Multi-Index Hashing scheme. This loss function ensures the hamming distance
equally distributed among all the contiguous disjoint sub-strings. To the best
of our knowledge, this is the first work in the iris indexing domain that
presents an end-to-end iris indexing structure. Experimental results on four
datasets are presented to depict the efficacy of the proposed approach. | [
"cs.CV",
"cs.AI"
] |
We propose a generative adversarial network with multiple discriminators,
where each discriminator is specialized to distinguish the subset of a real
dataset. This approach facilitates learning a generator coinciding with the
underlying data distribution and thus mitigates the chronic mode collapse
problem. From the inspiration of multiple choice learning, we guide each
discriminator to have expertise in the subset of the entire data and allow the
generator to find reasonable correspondences between the latent and real data
spaces automatically without supervision for training examples and the number
of discriminators. Despite the use of multiple discriminators, the backbone
networks are shared across the discriminators and the increase of training cost
is minimized. We demonstrate the effectiveness of our algorithm in the standard
datasets using multiple evaluation metrics. | [
"cs.LG"
] |
One of the main issues related to unsupervised machine learning is the cost
of processing and extracting useful information from large datasets. In this
work, we propose a classifier ensemble based on the transferable learning
capabilities of the CLIP neural network architecture in multimodal environments
(image and text) from social media. For this purpose, we used the InstaNY100K
dataset and proposed a validation approach based on sampling techniques. Our
experiments, based on image classification tasks according to the labels of the
Places dataset, are performed by first considering only the visual part, and
then adding the associated texts as support. The results obtained demonstrated
that trained neural networks such as CLIP can be successfully applied to image
classification with little fine-tuning, and considering the associated texts to
the images can help to improve the accuracy depending on the goal. The results
demonstrated what seems to be a promising research direction. | [
"cs.CV",
"cs.AI"
] |
The key idea behind the unsupervised learning of disentangled representations
is that real-world data is generated by a few explanatory factors of variation
which can be recovered by unsupervised learning algorithms. In this paper, we
provide a sober look at recent progress in the field and challenge some common
assumptions. We first theoretically show that the unsupervised learning of
disentangled representations is fundamentally impossible without inductive
biases on both the models and the data. Then, we train more than 12000 models
covering most prominent methods and evaluation metrics in a reproducible
large-scale experimental study on seven different data sets. We observe that
while the different methods successfully enforce properties ``encouraged'' by
the corresponding losses, well-disentangled models seemingly cannot be
identified without supervision. Furthermore, increased disentanglement does not
seem to lead to a decreased sample complexity of learning for downstream tasks.
Our results suggest that future work on disentanglement learning should be
explicit about the role of inductive biases and (implicit) supervision,
investigate concrete benefits of enforcing disentanglement of the learned
representations, and consider a reproducible experimental setup covering
several data sets. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative
Weights Update (OMWU) for saddle-point optimization have received growing
attention due to their favorable last-iterate convergence. However, their
behaviors for simple bilinear games over the probability simplex are still not
fully understood - previous analysis lacks explicit convergence rates, only
applies to an exponentially small learning rate, or requires additional
assumptions such as the uniqueness of the optimal solution. In this work, we
significantly expand the understanding of last-iterate convergence for OGDA and
OMWU in the constrained setting. Specifically, for OMWU in bilinear games over
the simplex, we show that when the equilibrium is unique, linear last-iterate
convergence is achieved with a learning rate whose value is set to a universal
constant, improving the result of (Daskalakis & Panageas, 2019b) under the same
assumption. We then significantly extend the results to more general objectives
and feasible sets for the projected OGDA algorithm, by introducing a sufficient
condition under which OGDA exhibits concrete last-iterate convergence rates
with a constant learning rate whose value only depends on the smoothness of the
objective function. We show that bilinear games over any polytope satisfy this
condition and OGDA converges exponentially fast even without the unique
equilibrium assumption. Our condition also holds for
strongly-convex-strongly-concave functions, recovering the result of (Hsieh et
al., 2019). Finally, we provide experimental results to further support our
theory. | [
"cs.LG",
"cs.GT",
"stat.ML"
] |
In standard generative adversarial network (SGAN), the discriminator
estimates the probability that the input data is real. The generator is trained
to increase the probability that fake data is real. We argue that it should
also simultaneously decrease the probability that real data is real because 1)
this would account for a priori knowledge that half of the data in the
mini-batch is fake, 2) this would be observed with divergence minimization, and
3) in optimal settings, SGAN would be equivalent to integral probability metric
(IPM) GANs.
We show that this property can be induced by using a relativistic
discriminator which estimate the probability that the given real data is more
realistic than a randomly sampled fake data. We also present a variant in which
the discriminator estimate the probability that the given real data is more
realistic than fake data, on average. We generalize both approaches to
non-standard GAN loss functions and we refer to them respectively as
Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that
IPM-based GANs are a subset of RGANs which use the identity function.
Empirically, we observe that 1) RGANs and RaGANs are significantly more
stable and generate higher quality data samples than their non-relativistic
counterparts, 2) Standard RaGAN with gradient penalty generate data of better
quality than WGAN-GP while only requiring a single discriminator update per
generator update (reducing the time taken for reaching the state-of-the-art by
400%), and 3) RaGANs are able to generate plausible high resolutions images
(256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these
images are of significantly better quality than the ones generated by WGAN-GP
and SGAN with spectral normalization. | [
"cs.LG",
"cs.AI",
"cs.CR",
"stat.ML"
] |
This paper describes a novel method for partitioning image into meaningful
segments. The proposed method employs watershed transform, a well-known image
segmentation technique. Along with that, it uses various auxiliary schemes such
as Binary Gradient Masking, dilation which segment the image in proper way. The
algorithm proposed in this paper considers all these methods in effective way
and takes little time. It is organized in such a manner so that it operates on
input image adaptively. Its robustness and efficiency makes it more convenient
and suitable for all types of images. | [
"cs.CV"
] |
Efficient software testing is essential for productive software development
and reliable user experiences. As human testing is inefficient and expensive,
automated software testing is needed. In this work, we propose a Reinforcement
Learning (RL) framework for functional software testing named DRIFT. DRIFT
operates on the symbolic representation of the user interface. It uses
Q-learning through Batch-RL and models the state-action value function with a
Graph Neural Network. We apply DRIFT to testing the Windows 10 operating system
and show that DRIFT can robustly trigger the desired software functionality in
a fully automated manner. Our experiments test the ability to perform single
and combined tasks across different applications, demonstrating that our
framework can efficiently test software with a large range of testing
objectives. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Datasets are crucial when training a deep neural network. When datasets are
unrepresentative, trained models are prone to bias because they are unable to
generalise to real world settings. This is particularly problematic for models
trained in specific cultural contexts, which may not represent a wide range of
races, and thus fail to generalise. This is a particular challenge for Driver
drowsiness detection, where many publicly available datasets are
unrepresentative as they cover only certain ethnicity groups. Traditional
augmentation methods are unable to improve a model's performance when tested on
other groups with different facial attributes, and it is often challenging to
build new, more representative datasets. In this paper, we introduce a novel
framework that boosts the performance of detection of drowsiness for different
ethnicity groups. Our framework improves Convolutional Neural Network (CNN)
trained for prediction by using Generative Adversarial networks (GAN) for
targeted data augmentation based on a population bias visualisation strategy
that groups faces with similar facial attributes and highlights where the model
is failing. A sampling method selects faces where the model is not performing
well, which are used to fine-tune the CNN. Experiments show the efficacy of our
approach in improving driver drowsiness detection for under represented
ethnicity groups. Here, models trained on publicly available datasets are
compared with a model trained using the proposed data augmentation strategy.
Although developed in the context of driver drowsiness detection, the proposed
framework is not limited to the driver drowsiness detection task, but can be
applied to other applications. | [
"cs.CV"
] |
Shape modelling (with methods that output shapes) is a new and important task
in Bayesian nonparametrics and bioinformatics. In this work, we focus on
Bayesian nonparametric methods for capturing shapes by partitioning a space
using curves. In related work, the classical Mondrian process is used to
partition spaces recursively with axis-aligned cuts, and is widely applied in
multi-dimensional and relational data. The Mondrian process outputs
hyper-rectangles. Recently, the random tessellation process was introduced as a
generalization of the Mondrian process, partitioning a domain with non-axis
aligned cuts in an arbitrary dimensional space, and outputting polytopes.
Motivated by these processes, in this work, we propose a novel parallelized
Bayesian nonparametric approach to partition a domain with curves, enabling
complex data-shapes to be acquired. We apply our method to HIV-1-infected human
macrophage image dataset, and also simulated datasets sets to illustrate our
approach. We compare to support vector machines, random forests and
state-of-the-art computer vision methods such as simple linear iterative
clustering super pixel image segmentation. We develop an R package that is
available at
\url{https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions}. | [
"stat.ML",
"cs.LG"
] |
We describe a DNN for video classification and captioning, trained
end-to-end, with shared features, to solve tasks at different levels of
granularity, exploring the link between granularity in a source task and the
quality of learned features for transfer learning. For solving the new task
domain in transfer learning, we freeze the trained encoder and fine-tune a
neural net on the target domain. We train on the Something-Something dataset
with over 220, 000 videos, and multiple levels of target granularity, including
50 action groups, 174 fine-grained action categories and captions.
Classification and captioning with Something-Something are challenging because
of the subtle differences between actions, applied to thousands of different
object classes, and the diversity of captions penned by crowd actors. Our model
performs better than existing classification baselines for SomethingSomething,
with impressive fine-grained results. And it yields a strong baseline on the
new Something-Something captioning task. Experiments reveal that training with
more fine-grained tasks tends to produce better features for transfer learning. | [
"cs.CV"
] |
Many versions of cross-validation (CV) exist in the literature; and each
version though has different variants. All are used interchangeably by many
practitioners; yet, without explanation to the connection or difference among
them. This article has three contributions. First, it starts by mathematical
formalization of these different versions and variants that estimate the error
rate and the Area Under the ROC Curve (AUC) of a classification rule, to show
the connection and difference among them. Second, we prove some of their
properties and prove that many variants are either redundant or "not smooth".
Hence, we suggest to abandon all redundant versions and variants and only keep
the leave-one-out, the $K$-fold, and the repeated $K$-fold. We show that the
latter is the only among the three versions that is "smooth" and hence looks
mathematically like estimating the mean performance of the classification
rules. However, empirically, for the known phenomenon of "weak correlation",
which we explain mathematically and experimentally, it estimates both
conditional and mean performance almost with the same accuracy. Third, we
conclude the article with suggesting two research points that may answer the
remaining question of whether we can come up with a finalist among the three
estimators: (1) a comparative study, that is much more comprehensive than those
available in literature and conclude no overall winner, is needed to consider a
wide range of distributions, datasets, and classifiers including complex ones
obtained via the recent deep learning approach. (2) we sketch the path of
deriving a rigorous method for estimating the variance of the only "smooth"
version, repeated $K$-fold CV, rather than those ad-hoc methods available in
the literature that ignore the covariance structure among the folds of CV. | [
"stat.ML",
"cs.LG"
] |
We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a
large-scale testbed for unsupervised domain adaptation across visual domains.
Unsupervised domain adaptation aims to solve the real-world problem of domain
shift, where machine learning models trained on one domain must be transferred
and adapted to a novel visual domain without additional supervision. The
VisDA2017 challenge is focused on the simulation-to-reality shift and has two
associated tasks: image classification and image segmentation. The goal in both
tracks is to first train a model on simulated, synthetic data in the source
domain and then adapt it to perform well on real image data in the unlabeled
test domain. Our dataset is the largest one to date for cross-domain object
classification, with over 280K images across 12 categories in the combined
training, validation and testing domains. The image segmentation dataset is
also large-scale with over 30K images across 18 categories in the three
domains. We compare VisDA to existing cross-domain adaptation datasets and
provide a baseline performance analysis using various domain adaptation models
that are currently popular in the field. | [
"cs.CV"
] |
Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial
node representations (i.e., the node representations before the first-time
graph convolution) largely affect the final model performance. However, when
learning the initial representation for a node, most existing work linearly
combines the embeddings of node features, without considering the interactions
among the features (or feature embeddings). We argue that when the node
features are categorical, e.g., in many real-world applications like user
profiling and recommender system, feature interactions usually carry important
signals for predictive analytics. Ignoring them will result in suboptimal
initial node representation and thus weaken the effectiveness of the follow-up
graph convolution. In this paper, we propose a new GCN model named CatGCN,
which is tailored for graph learning when the node features are categorical.
Specifically, we integrate two ways of explicit interaction modeling into the
learning of initial node representation, i.e., local interaction modeling on
each pair of node features and global interaction modeling on an artificial
feature graph. We then refine the enhanced initial node representations with
the neighborhood aggregation-based graph convolution. We train CatGCN in an
end-to-end fashion and demonstrate it on semi-supervised node classification.
Extensive experiments on three tasks of user profiling (the prediction of user
age, city, and purchase level) from Tencent and Alibaba datasets validate the
effectiveness of CatGCN, especially the positive effect of performing feature
interaction modeling before graph convolution. | [
"cs.LG",
"stat.ML"
] |
Convolutional Neural Networks (CNNs) have demonstrated great results for the
single-image super-resolution (SISR) problem. Currently, most CNN algorithms
promote deep and computationally expensive models to solve SISR. However, we
propose a novel SISR method that uses relatively less number of computations.
On training, we get group convolutions that have unused connections removed. We
have refined this system specifically for the task at hand by removing
unnecessary modules from original CondenseNet. Further, a reconstruction
network consisting of deconvolutional layers has been used in order to upscale
to high resolution. All these steps significantly reduce the number of
computations required at testing time. Along with this, bicubic upsampled input
is added to the network output for easier learning. Our model is named
SRCondenseNet. We evaluate the method using various benchmark datasets and show
that it performs favourably against the state-of-the-art methods in terms of
both accuracy and number of computations required. | [
"cs.CV"
] |
Learning to classify time series with limited data is a practical yet
challenging problem. Current methods are primarily based on hand-designed
feature extraction rules or domain-specific data augmentation. Motivated by the
advances in deep speech processing models and the fact that voice data are
univariate temporal signals, in this paper, we propose Voice2Series (V2S), a
novel end-to-end approach that reprograms acoustic models for time series
classification, through input transformation learning and output label mapping.
Leveraging the representation learning power of a large-scale pre-trained
speech processing model, on 30 different time series tasks we show that V2S
either outperforms or is tied with state-of-the-art methods on 20 tasks, and
improves their average accuracy by 1.84%. We further provide a theoretical
justification of V2S by proving its population risk is upper bounded by the
source risk and a Wasserstein distance accounting for feature alignment via
reprogramming. Our results offer new and effective means to time series
classification. | [
"cs.LG",
"cs.AI",
"cs.NE",
"cs.SD",
"eess.AS"
] |
Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight
(ToF) are usually contaminated by missing data, noises and suffer from being of
low resolution. In this paper, we present a robust method for high-quality
restoration of a degraded depth map with the guidance of the corresponding
color image. We solve the problem in an energy optimization framework that
consists of a novel robust data term and smoothness term. To accommodate not
only the noise but also the inconsistency between depth discontinuities and the
color edges, we model both the data term and smoothness term with a robust
exponential error norm function. We propose to use Iteratively Re-weighted
Least Squares (IRLS) methods for efficiently solving the resulting highly
non-convex optimization problem. More importantly, we further develop a
data-driven adaptive parameter selection scheme to properly determine the
parameter in the model. We show that the proposed approach can preserve fine
details and sharp depth discontinuities even for a large upsampling factor
($8\times$ for example). Experimental results on both simulated and real
datasets demonstrate that the proposed method outperforms recent
state-of-the-art methods in coping with the heavy noise, preserving sharp depth
discontinuities and suppressing the texture copy artifacts. | [
"cs.CV"
] |
In this paper, we present a deep learning architecture which addresses the
problem of 3D semantic segmentation of unstructured point clouds. Compared to
previous work, we introduce grouping techniques which define point
neighborhoods in the initial world space and the learned feature space.
Neighborhoods are important as they allow to compute local or global point
features depending on the spatial extend of the neighborhood. Additionally, we
incorporate dedicated loss functions to further structure the learned point
feature space: the pairwise distance loss and the centroid loss. We show how to
apply these mechanisms to the task of 3D semantic segmentation of point clouds
and report state-of-the-art performance on indoor and outdoor datasets. | [
"cs.CV"
] |
In this work, we propose a method for three-dimensional (3D) reconstruction
of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM)
approach. We used a Kinect V2 Time-of-Flight (TOF) RGB-D camera to provide
colored dense point clouds at a 30 Hz frequency. This device is moved freely (6
degrees of freedom) during the scene exploration. The implemented SLAM solution
aligns successive point clouds using an 3D keypoints description and matching
approach. This type of approach exploits both colorimetric and geometrical
information, and permits reconstruction under poor illumination conditions. Our
solution has been tested for indoor crime scene and outdoor archaeological site
reconstruction, returning a mean error around one centimeter. It is less
precise than environmental laser scanner solution, but more practical and
portable as well as less cumbersome. Also, the hardware is definitively
cheaper. | [
"cs.CV"
] |
Machine learning has become a major field of research in order to handle more
and more complex image detection problems. Among the existing state-of-the-art
CNN models, in this paper a region-based, fully convolutional network, for fast
and accurate object detection has been proposed based on the experimental
results. Among the region based networks, ResNet is regarded as the most recent
CNN architecture which has obtained the best results at ImageNet Large-Scale
Visual Recognition Challenge (ILSVRC) in 2015. Deep residual networks (ResNets)
can make the training process faster and attain more accuracy compared to their
equivalent conventional neural networks. Being motivated with such unique
attributes of ResNet, this paper evaluates the performance of fine-tuned ResNet
for object classification of our weeds dataset. The dataset of farm land weeds
detection is insufficient to train such deep CNN models. To overcome this
shortcoming, we perform dropout techniques along with deep residual network for
reducing over-fitting problem as well as applying data augmentation with the
proposed ResNet to achieve a significant outperforming result from our weeds
dataset. We achieved better object detection performance with Region-based
Fully Convolutional Networks (R-FCN) technique which is latched with our
proposed ResNet-101. | [
"cs.CV",
"eess.IV"
] |
This paper presents F-Siamese Tracker, a novel approach for single object
tracking prominently characterized by more robustly integrating 2D and 3D
information to reduce redundant search space. A main challenge in 3D single
object tracking is how to reduce search space for generating appropriate 3D
candidates. Instead of solely relying on 3D proposals, firstly, our method
leverages the Siamese network applied on RGB images to produce 2D region
proposals which are then extruded into 3D viewing frustums. Besides, we perform
an online accuracy validation on the 3D frustum to generate refined point cloud
searching space, which can be embedded directly into the existing 3D tracking
backbone. For efficiency, our approach gains better performance with fewer
candidates by reducing search space. In addition, benefited from introducing
the online accuracy validation, for occasional cases with strong occlusions or
very sparse points, our approach can still achieve high precision, even when
the 2D Siamese tracker loses the target. This approach allows us to set a new
state-of-the-art in 3D single object tracking by a significant margin on a
sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single
object tracking show that our framework boosts 2D tracking performance as well. | [
"cs.CV"
] |
The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects. | [
"cs.CV"
] |
Many applications, including natural language processing, sensor networks,
collaborative filtering, and federated learning, call for estimating discrete
distributions from data collected in batches, some of which may be
untrustworthy, erroneous, faulty, or even adversarial.
Previous estimators for this setting ran in exponential time, and for some
regimes required a suboptimal number of batches. We provide the first
polynomial-time estimator that is optimal in the number of batches and achieves
essentially the best possible estimation accuracy. | [
"cs.LG",
"stat.ML"
] |
Underwater image enhancement is an important low-level computer vision task
for autonomous underwater vehicles and remotely operated vehicles to explore
and understand the underwater environments. Recently, deep convolutional neural
networks (CNNs) have been successfully used in many computer vision problems,
and so does underwater image enhancement. There are many deep-learning-based
methods with impressive performance for underwater image enhancement, but their
memory and model parameter costs are hindrances in practical application. To
address this issue, we propose a lightweight adaptive feature fusion network
(LAFFNet). The model is the encoder-decoder model with multiple adaptive
feature fusion (AAF) modules. AAF subsumes multiple branches with different
kernel sizes to generate multi-scale feature maps. Furthermore, channel
attention is used to merge these feature maps adaptively. Our method reduces
the number of parameters from 2.5M to 0.15M (around 94% reduction) but
outperforms state-of-the-art algorithms by extensive experiments. Furthermore,
we demonstrate our LAFFNet effectively improves high-level vision tasks like
salience object detection and single image depth estimation. | [
"cs.CV"
] |
Convolutional neural networks (CNN) have made significant advances in
hyperspectral image (HSI) classification. However, standard convolutional
kernel neglects the intrinsic connections between data points, resulting in
poor region delineation and small spurious predictions. Furthermore, HSIs have
a unique continuous data distribution along the high dimensional spectrum
domain - much remains to be addressed in characterizing the spectral contexts
considering the prohibitively high dimensionality and improving reasoning
capability in light of the limited amount of labelled data. This paper presents
a novel architecture which explicitly addresses these two issues. Specifically,
we design an architecture to encode the multiple spectral contextual
information in the form of spectral pyramid of multiple embedding spaces. In
each spectral embedding space, we propose graph attention mechanism to
explicitly perform interpretable reasoning in the spatial domain based on the
connection in spectral feature space. Experiments on three HSI datasets
demonstrate that the proposed architecture can significantly improve the
classification accuracy compared with the existing methods. | [
"cs.CV"
] |
State-of-the-art (SOTA) Generative Models (GMs) can synthesize
photo-realistic images that are hard for humans to distinguish from genuine
photos. We propose to perform reverse engineering of GMs to infer the model
hyperparameters from the images generated by these models. We define a novel
problem, "model parsing", as estimating GM network architectures and training
loss functions by examining their generated images -- a task seemingly
impossible for human beings. To tackle this problem, we propose a framework
with two components: a Fingerprint Estimation Network (FEN), which estimates a
GM fingerprint from a generated image by training with four constraints to
encourage the fingerprint to have desired properties, and a Parsing Network
(PN), which predicts network architecture and loss functions from the estimated
fingerprints. To evaluate our approach, we collect a fake image dataset with
$100$K images generated by $100$ GMs. Extensive experiments show encouraging
results in parsing the hyperparameters of the unseen models. Finally, our
fingerprint estimation can be leveraged for deepfake detection and image
attribution, as we show by reporting SOTA results on both the recent Celeb-DF
and image attribution benchmarks. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Recent research advances in Computer Vision and Natural Language Processing
have introduced novel tasks that are paving the way for solving AI-complete
problems. One of those tasks is called Visual Question Answering (VQA). A VQA
system must take an image and a free-form, open-ended natural language question
about the image, and produce a natural language answer as the output. Such a
task has drawn great attention from the scientific community, which generated a
plethora of approaches that aim to improve the VQA predictive accuracy. Most of
them comprise three major components: (i) independent representation learning
of images and questions; (ii) feature fusion so the model can use information
from both sources to answer visual questions; and (iii) the generation of the
correct answer in natural language. With so many approaches being recently
introduced, it became unclear the real contribution of each component for the
ultimate performance of the model. The main goal of this paper is to provide a
comprehensive analysis regarding the impact of each component in VQA models.
Our extensive set of experiments cover both visual and textual elements, as
well as the combination of these representations in form of fusion and
attention mechanisms. Our major contribution is to identify core components for
training VQA models so as to maximize their predictive performance. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In this paper, we propose Stacked Generative Adversarial Networks
(StackGAN) to generate 256x256 photo-realistic images conditioned on text
descriptions. We decompose the hard problem into more manageable sub-problems
through a sketch-refinement process. The Stage-I GAN sketches the primitive
shape and colors of the object based on the given text description, yielding
Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text
descriptions as inputs, and generates high-resolution images with
photo-realistic details. It is able to rectify defects in Stage-I results and
add compelling details with the refinement process. To improve the diversity of
the synthesized images and stabilize the training of the conditional-GAN, we
introduce a novel Conditioning Augmentation technique that encourages
smoothness in the latent conditioning manifold. Extensive experiments and
comparisons with state-of-the-arts on benchmark datasets demonstrate that the
proposed method achieves significant improvements on generating photo-realistic
images conditioned on text descriptions. | [
"cs.CV",
"cs.AI",
"stat.ML"
] |
Reinforcement learning (RL) typically defines a discount factor as part of
the Markov Decision Process. The discount factor values future rewards by an
exponential scheme that leads to theoretical convergence guarantees of the
Bellman equation. However, evidence from psychology, economics and neuroscience
suggests that humans and animals instead have hyperbolic time-preferences. In
this work we revisit the fundamentals of discounting in RL and bridge this
disconnect by implementing an RL agent that acts via hyperbolic discounting. We
demonstrate that a simple approach approximates hyperbolic discount functions
while still using familiar temporal-difference learning techniques in RL.
Additionally, and independent of hyperbolic discounting, we make a surprising
discovery that simultaneously learning value functions over multiple
time-horizons is an effective auxiliary task which often improves over a strong
value-based RL agent, Rainbow. | [
"stat.ML",
"cs.LG"
] |
Generative Adversarial networks (GANs) have obtained remarkable success in
many unsupervised learning tasks and unarguably, clustering is an important
unsupervised learning problem. While one can potentially exploit the
latent-space back-projection in GANs to cluster, we demonstrate that the
cluster structure is not retained in the GAN latent space.
In this paper, we propose ClusterGAN as a new mechanism for clustering using
GANs. By sampling latent variables from a mixture of one-hot encoded variables
and continuous latent variables, coupled with an inverse network (which
projects the data to the latent space) trained jointly with a clustering
specific loss, we are able to achieve clustering in the latent space. Our
results show a remarkable phenomenon that GANs can preserve latent space
interpolation across categories, even though the discriminator is never exposed
to such vectors. We compare our results with various clustering baselines and
demonstrate superior performance on both synthetic and real datasets. | [
"cs.LG",
"stat.ML"
] |
Herein, we present a system for hyperspectral image segmentation that
utilizes multiple class--based denoising autoencoders which are efficiently
trained. Moreover, we present a novel hyperspectral data augmentation method
for labelled HSI data using linear mixtures of pixels from each class, which
helps the system with edge pixels which are almost always mixed pixels.
Finally, we utilize a deep neural network and morphological hole-filling to
provide robust image classification. Results run on the Salinas dataset verify
the high performance of the proposed algorithm. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Recently, deep learning based single image super-resolution(SR) approaches
have achieved great development. The state-of-the-art SR methods usually adopt
a feed-forward pipeline to establish a non-linear mapping between low-res(LR)
and high-res(HR) images. However, due to treating all image regions equally
without considering the difficulty diversity, these approaches meet an upper
bound for optimization. To address this issue, we propose a novel SR approach
that discriminately processes each image region within an image by its
difficulty. Specifically, we propose a dual-way SR network that one way is
trained to focus on easy image regions and another is trained to handle hard
image regions. To identify whether a region is easy or hard, we propose a novel
image difficulty recognition network based on PSNR prior. Our SR approach that
uses the region mask to adaptively enforce the dual-way SR network yields
superior results. Extensive experiments on several standard benchmarks (e.g.,
Set5, Set14, BSD100, and Urban100) show that our approach achieves
state-of-the-art performance. | [
"cs.CV"
] |
For complex segmentation tasks, fully automatic systems are inherently
limited in their achievable accuracy for extracting relevant objects.
Especially in cases where only few data sets need to be processed for a highly
accurate result, semi-automatic segmentation techniques exhibit a clear benefit
for the user. One area of application is medical image processing during an
intervention for a single patient. We propose a learning-based cooperative
segmentation approach which includes the computing entity as well as the user
into the task. Our system builds upon a state-of-the-art fully convolutional
artificial neural network (FCN) as well as an active user model for training.
During the segmentation process, a user of the trained system can iteratively
add additional hints in form of pictorial scribbles as seed points into the FCN
system to achieve an interactive and precise segmentation result. The
segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches
can yield superior results compared to networks without the user input channel
component, due to a consistent improvement in segmentation quality after each
interaction. | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.NE",
"68T05, 68T45",
"I.2.6; I.4.6; I.5.5"
] |
In short, our experiments suggest that yes, on average, rotation forest is
better than the most common alternatives when all the attributes are
real-valued. Rotation forest is a tree based ensemble that performs transforms
on subsets of attributes prior to constructing each tree. We present an
empirical comparison of classifiers for problems with only real-valued
features. We evaluate classifiers from three families of algorithms: support
vector machines; tree-based ensembles; and neural networks tuned with a large
grid search. We compare classifiers on unseen data based on the quality of the
decision rule (using classification error) the ability to rank cases (area
under the receiver operating characteristic) and the probability estimates
(using negative log likelihood). We conclude that, in answer to the question
posed in the title, yes, rotation forest is significantly more accurate on
average than competing techniques when compared on three distinct sets of
datasets. Further, we assess the impact of the design features of rotation
forest through an ablative study that transforms random forest into rotation
forest. We identify the major limitation of rotation forest as its scalability,
particularly in number of attributes. To overcome this problem we develop a
model to predict the train time of the algorithm and hence propose a contract
version of rotation forest where a run time cap is imposed {\em a priori}. We
demonstrate that on large problems rotation forest can be made an order of
magnitude faster without significant loss of accuracy. We also show that there
is no real benefit (on average) from tuning rotation forest. We maintain that
without any domain knowledge to indicate an algorithm preference, rotation
forest should be the default algorithm of choice for problems with continuous
attributes. | [
"cs.LG",
"stat.ML"
] |
Recent studies show that deep neural networks are vulnerable to adversarial
examples which can be generated via certain types of transformations. Being
robust to a desired family of adversarial attacks is then equivalent to being
invariant to a family of transformations. Learning invariant representations
then naturally emerges as an important goal to achieve which we explore in this
paper within specific application contexts. Specifically, we propose a
cyclically-trained adversarial network to learn a mapping from image space to
latent representation space and back such that the latent representation is
invariant to a specified factor of variation (e.g., identity). The learned
mapping assures that the synthesized image is not only realistic, but has the
same values for unspecified factors (e.g., pose and illumination) as the
original image and a desired value of the specified factor. Unlike disentangled
representation learning, which requires two latent spaces, one for specified
and another for unspecified factors, invariant representation learning needs
only one such space. We encourage invariance to a specified factor by applying
adversarial training using a variational autoencoder in the image space as
opposed to the latent space. We strengthen this invariance by introducing a
cyclic training process (forward and backward cycle). We also propose a new
method to evaluate conditional generative networks. It compares how well
different factors of variation can be predicted from the synthesized, as
opposed to real, images. In quantitative terms, our approach attains
state-of-the-art performance in experiments spanning three datasets with
factors such as identity, pose, illumination or style. Our method produces
sharp, high-quality synthetic images with little visible artefacts compared to
previous approaches. | [
"cs.CV"
] |
With the immersive development in the field of augmented and virtual reality,
accurate and speedy eye-tracking is required. Facebook Research has organized a
challenge, named OpenEDS Semantic Segmentation challenge for per-pixel
segmentation of the key eye regions: the sclera, the iris, the pupil, and
everything else (background). There are two constraints set for the
participants viz MIOU and the computational complexity of the model. More
recently, researchers have achieved quite a good result using the convolutional
neural networks (CNN) in segmenting eyeregions. However, the environmental
challenges involved in this task such as low resolution, blur, unusual glint
and, illumination, off-angles, off-axis, use of glasses and different color of
iris region hinder the accuracy of segmentation. To address the challenges in
eye segmentation, the present work proposes a robust and computationally
efficient attention-based convolutional encoder-decoder network for segmenting
all the eye regions. Our model, named EyeNet, includes modified residual units
as the backbone, two types of attention blocks and multi-scale supervision for
segmenting the aforesaid four eye regions. Our proposed model achieved a total
score of 0.974(EDS Evaluation metric) on test data, which demonstrates superior
results compared to the baseline methods. | [
"cs.CV"
] |
Deep Neural Networks have now achieved state-of-the-art results in a wide
range of tasks including image classification, object detection and so on.
However, they are both computation consuming and memory intensive, making them
difficult to deploy on low-power devices. Network binarization is one of the
existing effective techniques for model compression and acceleration, but there
is no computing kernel yet to support it on PyTorch. In this paper we developed
a computing kernel supporting 1-bit xnor and bitcount computation on PyTorch.
Experimental results show that our kernel could accelerate the inference of the
binarized neural network by 3 times in GPU and by 4.5 times in CPU compared
with the control group. | [
"cs.LG",
"cs.NE"
] |
The drone navigation requires the comprehensive understanding of both visual
and geometric information in the 3D world. In this paper, we present a
Visual-Geometric Fusion Network(VGF-Net), a deep network for the fusion
analysis of visual/geometric data and the construction of 2.5D height maps for
simultaneous drone navigation in novel environments. Given an initial rough
height map and a sequence of RGB images, our VGF-Net extracts the visual
information of the scene, along with a sparse set of 3D keypoints that capture
the geometric relationship between objects in the scene. Driven by the data,
VGF-Net adaptively fuses visual and geometric information, forming a unified
Visual-Geometric Representation. This representation is fed to a new
Directional Attention Model(DAM), which helps enhance the visual-geometric
object relationship and propagates the informative data to dynamically refine
the height map and the corresponding keypoints. An entire end-to-end
information fusion and mapping system is formed, demonstrating remarkable
robustness and high accuracy on the autonomous drone navigation across complex
indoor and large-scale outdoor scenes. The dataset can be found in
http://vcc.szu.edu.cn/research/2021/VGFNet. | [
"cs.CV"
] |
In this article, we propose a new variational approach to learn private
and/or fair representations. This approach is based on the Lagrangians of a new
formulation of the privacy and fairness optimization problems that we propose.
In this formulation, we aim to generate representations of the data that keep a
prescribed level of the relevant information that is not shared by the private
or sensitive data, while minimizing the remaining information they keep. The
proposed approach (i) exhibits the similarities of the privacy and fairness
problems, (ii) allows us to control the trade-off between utility and privacy
or fairness through the Lagrange multiplier parameter, and (iii) can be
comfortably incorporated to common representation learning algorithms such as
the VAE, the $\beta$-VAE, the VIB, or the nonlinear IB. | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT"
] |
This article proposes two different approaches to automatically create a map
for valid on-street car parking spaces. For this, we use car sharing park-out
events data. The first one uses spatial aggregation and the second a machine
learning algorithm. For the former, we chose rasterization and road sectioning;
for the latter we chose decision trees. We compare the results of these
approaches and discuss their advantages and disadvantages. Furthermore, we show
our results for a neighborhood in the city of Berlin and report a
classification accuracy of 91.6\% on the original imbalanced data. Finally, we
discuss further work; from gathering more data over a longer period of time to
fitting spatial Gaussian densities to the data and the usage of apps for manual
validation and annotation of parking spaces to improve ground truth data. | [
"cs.LG"
] |
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique
used to increase the interpretability and explainability of black box Machine
Learning (ML) algorithms. LIME typically generates an explanation for a single
prediction by any ML model by learning a simpler interpretable model (e.g.
linear classifier) around the prediction through generating simulated data
around the instance by random perturbation, and obtaining feature importance
through applying some form of feature selection. While LIME and similar local
algorithms have gained popularity due to their simplicity, the random
perturbation and feature selection methods result in "instability" in the
generated explanations, where for the same prediction, different explanations
can be generated. This is a critical issue that can prevent deployment of LIME
in a Computer-Aided Diagnosis (CAD) system, where stability is of utmost
importance to earn the trust of medical professionals. In this paper, we
propose a deterministic version of LIME. Instead of random perturbation, we
utilize agglomerative Hierarchical Clustering (HC) to group the training data
together and K-Nearest Neighbour (KNN) to select the relevant cluster of the
new instance that is being explained. After finding the relevant cluster, a
linear model is trained over the selected cluster to generate the explanations.
Experimental results on three different medical datasets show the superiority
for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME),
where we quantitatively determine the stability of DLIME compared to LIME
utilizing the Jaccard similarity among multiple generated explanations. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
To investigate whether the vanishing point (VP) plays a significant role in
gaze guidance, we ran two experiments. In the first one, we recorded fixations
of 10 observers (4 female; mean age 22; SD=0.84) freely viewing 532 images, out
of which 319 had VP (shuffled presentation; each image for 4 secs). We found
that the average number of fixations at a local region (80x80 pixels) centered
at the VP is significantly higher than the average fixations at random
locations (t-test; n=319; p=1.8e-35). To address the confounding factor of
saliency, we learned a combined model of bottom-up saliency and VP. AUC score
of our model (0.85; SD=0.01) is significantly higher than the original saliency
model (e.g., 0.8 using AIM model by Bruce & Tsotsos (2009), t-test; p=
3.14e-16) and the VP-only model (0.64, t-test; p= 4.02e-22). In the second
experiment, we asked 14 subjects (4 female, mean age 23.07, SD=1.26) to search
for a target character (T or L) placed randomly on a 3x3 imaginary grid
overlaid on top of an image. Subjects reported their answers by pressing one of
two keys. Stimuli consisted of 270 color images (180 with a single VP, 90
without). The target happened with equal probability inside each cell (15 times
L, 15 times T). We found that subjects were significantly faster (and more
accurate) when target happened inside the cell containing the VP compared to
cells without VP (median across 14 subjects 1.34 sec vs. 1.96; Wilcoxon
rank-sum test; p = 0.0014). Response time at VP cells were also significantly
lower than response time on images without VP (median 2.37; p= 4.77e-05). These
findings support the hypothesis that vanishing point, similar to face and text
(Cerf et al., 2009) as well as gaze direction (Borji et al., 2014) attracts
attention in free-viewing and visual search. | [
"cs.CV"
] |
This paper introduces a novel deep learning based method, named bridge neural
network (BNN) to dig the potential relationship between two given data sources
task by task. The proposed approach employs two convolutional neural networks
that project the two data sources into a feature space to learn the desired
common representation required by the specific task. The training objective
with artificial negative samples is introduced with the ability of mini-batch
training and it's asymptotically equivalent to maximizing the total correlation
of the two data sources, which is verified by the theoretical analysis. The
experiments on the tasks, including pair matching, canonical correlation
analysis, transfer learning, and reconstruction demonstrate the
state-of-the-art performance of BNN, which may provide new insights into the
aspect of common representation learning. | [
"cs.LG",
"stat.ML"
] |
Fully convolutional neural networks (CNNs) have proven to be effective at
representing and classifying textural information, thus transforming image
intensity into output class masks that achieve semantic image segmentation. In
medical image analysis, however, expert manual segmentation often relies on the
boundaries of anatomical structures of interest. We propose boundary aware CNNs
for medical image segmentation. Our networks are designed to account for organ
boundary information, both by providing a special network edge branch and
edge-aware loss terms, and they are trainable end-to-end. We validate their
effectiveness on the task of brain tumor segmentation using the BraTS 2018
dataset. Our experiments reveal that our approach yields more accurate
segmentation results, which makes it promising for more extensive application
to medical image segmentation. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in
Chemistry, is a technology for determining the 3-D structure of macromolecules
from many noisy 2-D projections of instances of these macromolecules, whose
orientations and positions are unknown. The molecular structures are not rigid
objects, but flexible objects involved in dynamical processes. The different
conformations are exhibited by different instances of the macromolecule
observed in a cryo-EM experiment, each of which is recorded as a particle
image. The range of conformations and the conformation of each particle are not
known a priori; one of the great promises of cryo-EM is to map this
conformation space. Remarkable progress has been made in determining rigid
structures from homogeneous samples of molecules in spite of the unknown
orientation of each particle image and significant progress has been made in
recovering a few distinct states from mixtures of rather distinct
conformations, but more complex heterogeneous samples remain a major challenge.
We introduce the ``hyper-molecule'' framework for modeling structures across
different states of heterogeneous molecules, including continuums of states.
The key idea behind this framework is representing heterogeneous macromolecules
as high-dimensional objects, with the additional dimensions representing the
conformation space. This idea is then refined to model properties such as
localized heterogeneity. In addition, we introduce an algorithmic framework for
recovering such maps of heterogeneous objects from experimental data using a
Bayesian formulation of the problem and Markov chain Monte Carlo (MCMC)
algorithms to address the computational challenges in recovering these high
dimensional hyper-molecules. We demonstrate these ideas in a prototype applied
to synthetic data. | [
"cs.CV",
"stat.AP"
] |
Generative adversarial networks (GANs) are an expressive class of neural
generative models with tremendous success in modeling high-dimensional
continuous measures. In this paper, we present a scalable method for unbalanced
optimal transport (OT) based on the generative-adversarial framework. We
formulate unbalanced OT as a problem of simultaneously learning a transport map
and a scaling factor that push a source measure to a target measure in a
cost-optimal manner. In addition, we propose an algorithm for solving this
problem based on stochastic alternating gradient updates, similar in practice
to GANs. We also provide theoretical justification for this formulation,
showing that it is closely related to an existing static formulation by Liero
et al. (2018), and perform numerical experiments demonstrating how this
methodology can be applied to population modeling. | [
"cs.LG",
"stat.ML",
"68T99"
] |
Feature-based transfer is one of the most effective methodologies for
transfer learning. Existing studies usually assume that the learned new feature
representation is truly \emph{domain-invariant}, and thus directly train a
transfer model $\mathcal{M}$ on source domain. In this paper, we consider a
more realistic scenario where the new feature representation is suboptimal and
small divergence still exists across domains. We propose a new learning
strategy with a transfer model called Randomized Transferable Machine (RTM).
More specifically, we work on source data with the new feature representation
learned from existing feature-based transfer methods. The key idea is to
enlarge source training data populations by randomly corrupting source data
using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$
that performs well on all the corrupted source data populations. In principle,
the more corruptions are made, the higher the probability of the target data
can be covered by the constructed source populations, and thus better transfer
performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with
infinite corruptions, which however is infeasible in reality. We develop a
marginalized solution with linear regression model and dropout noise. With a
marginalization trick, we can train an RTM that is equivalently to training
using infinite source noisy populations without truly conducting any
corruption. More importantly, such an RTM has a closed-form solution, which
enables very fast and efficient training. Extensive experiments on various
real-world transfer tasks show that RTM is a promising transfer model. | [
"cs.LG",
"cs.AI"
] |
Studying neural connectivity is considered one of the most promising and
challenging areas of modern neuroscience. The underpinnings of cognition are
hidden in the way neurons interact with each other. However, our experimental
methods of studying real neural connections at a microscopic level are still
arduous and costly. An efficient alternative is to infer connectivity based on
the neuronal activations using computational methods. A reliable method for
network inference, would not only facilitate research of neural circuits
without the need of laborious experiments but also reveal insights on the
underlying mechanisms of the brain. In this work, we perform a review of
methods for neural circuit inference given the activation time series of the
neural population. Approaching it from machine learning perspective, we divide
the methodologies into unsupervised and supervised learning. The methods are
based on correlation metrics, probabilistic point processes, and neural
networks. Furthermore, we add a data mining methodology inspired by influence
estimation in social networks as a new supervised learning approach. For
comparison, we use the small version of the Chalearn Connectomics competition,
that is accompanied with ground truth connections between neurons. The
experiments indicate that unsupervised learning methods perform better,
however, supervised methods could surpass them given enough data and resources. | [
"stat.ML",
"cs.LG"
] |
Convolutional neural networks (CNNs) learn filters in order to capture local
correlation patterns in feature space. We propose to learn these filters as
combinations of preset spectral filters defined by the Discrete Cosine
Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional
convolutional layers to produce partially or fully harmonic versions of new or
existing CNN architectures. Using DCT energy compaction properties, we
demonstrate how the harmonic networks can be efficiently compressed by
truncating high-frequency information in harmonic blocks thanks to the
redundancies in the spectral domain. We report extensive experimental
validation demonstrating benefits of the introduction of harmonic blocks into
state-of-the-art CNN models in image classification, object detection and
semantic segmentation applications. | [
"cs.CV",
"cs.LG"
] |
In this paper, we propose GOHOME, a method leveraging graph representations
of the High Definition Map and sparse projections to generate a heatmap output
representing the future position probability distribution for a given agent in
a traffic scene. This heatmap output yields an unconstrained 2D grid
representation of agent future possible locations, allowing inherent
multimodality and a measure of the uncertainty of the prediction. Our
graph-oriented model avoids the high computation burden of representing the
surrounding context as squared images and processing it with classical CNNs,
but focuses instead only on the most probable lanes where the agent could end
up in the immediate future. GOHOME reaches 2$nd$ on Argoverse Motion
Forecasting Benchmark on the MissRate$_6$ metric while achieving significant
speed-up and memory burden diminution compared to Argoverse 1$^{st}$ place
method HOME. We also highlight that heatmap output enables multimodal
ensembling and improve 1$^{st}$ place MissRate$_6$ by more than 15$\%$ with our
best ensemble on Argoverse. Finally, we evaluate and reach state-of-the-art
performance on the other trajectory prediction datasets nuScenes and
Interaction, demonstrating the generalizability of our method. | [
"cs.CV",
"cs.RO"
] |
2D fully convolutional network has been recently successfully applied to
object detection from images. In this paper, we extend the fully convolutional
network based detection techniques to 3D and apply it to point cloud data. The
proposed approach is verified on the task of vehicle detection from lidar point
cloud for autonomous driving. Experiments on the KITTI dataset shows a
significant performance improvement over the previous point cloud based
detection approaches. | [
"cs.CV",
"cs.RO"
] |
Domain shift is unavoidable in real-world applications of object detection.
For example, in self-driving cars, the target domain consists of unconstrained
road environments which cannot all possibly be observed in training data.
Similarly, in surveillance applications sufficiently representative training
data may be lacking due to privacy regulations. In this paper, we address the
domain adaptation problem from the perspective of robust learning and show that
the problem may be formulated as training with noisy labels. We propose a
robust object detection framework that is resilient to noise in bounding box
class labels, locations and size annotations. To adapt to the domain shift, the
model is trained on the target domain using a set of noisy object bounding
boxes that are obtained by a detection model trained only in the source domain.
We evaluate the accuracy of our approach in various source/target domain pairs
and demonstrate that the model significantly improves the state-of-the-art on
multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI
datasets. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
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