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The study of genetic variants can help find correlating population groups to
identify cohorts that are predisposed to common diseases and explain
differences in disease susceptibility and how patients react to drugs. Machine
learning algorithms are increasingly being applied to identify interacting GVs
to understand their complex phenotypic traits. Since the performance of a
learning algorithm not only depends on the size and nature of the data but also
on the quality of underlying representation, deep neural networks can learn
non-linear mappings that allow transforming GVs data into more clustering and
classification friendly representations than manual feature selection. In this
paper, we proposed convolutional embedded networks in which we combine two DNN
architectures called convolutional embedded clustering and convolutional
autoencoder classifier for clustering individuals and predicting geographic
ethnicity based on GVs, respectively. We employed CAE-based representation
learning on 95 million GVs from the 1000 genomes and Simons genome diversity
projects. Quantitative and qualitative analyses with a focus on accuracy and
scalability show that our approach outperforms state-of-the-art approaches such
as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted
population groups in 22 hours with an adjusted rand index of 0.915, the
normalized mutual information of 0.92, and the clustering accuracy of 89%.
Contrarily, the CAE classifier can predict the geographic ethnicity of unknown
samples with an F1 and Mathews correlation coefficient(MCC) score of 0.9004 and
0.8245, respectively. To provide interpretations of the predictions, we
identify significant biomarkers using gradient boosted trees(GBT) and SHAP.
Overall, our approach is transparent and faster than the baseline methods, and
scalable for 5% to 100% of the full human genome. | [
"cs.LG",
"q-bio.QM",
"stat.ML"
] |
In the present study, we propose to implement a new framework for estimating
generative models via an adversarial process to extend an existing GAN
framework and develop a white-box controllable image cartoonization, which can
generate high-quality cartooned images/videos from real-world photos and
videos. The learning purposes of our system are based on three distinct
representations: surface representation, structure representation, and texture
representation. The surface representation refers to the smooth surface of the
images. The structure representation relates to the sparse colour blocks and
compresses generic content. The texture representation shows the texture,
curves, and features in cartoon images. Generative Adversarial Network (GAN)
framework decomposes the images into different representations and learns from
them to generate cartoon images. This decomposition makes the framework more
controllable and flexible which allows users to make changes based on the
required output. This approach overcomes any previous system in terms of
maintaining clarity, colours, textures, shapes of images yet showing the
characteristics of cartoon images. | [
"cs.CV",
"cs.LG"
] |
We present a new lightweight CNN-based algorithm for multi-frame optical flow
estimation. Our solution introduces a double recurrence over spatial scale and
time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell.
It includes (i) a temporal recurrence based on conveying learned features
rather than optical flow estimates; (ii) an occlusion detection process which
is coupled with optical flow estimation and therefore uses a very limited
number of extra parameters. The resulting STaRFlow algorithm gives
state-of-the-art performances on MPI Sintel and Kitti2015 and involves
significantly less parameters than all other methods with comparable results. | [
"cs.CV"
] |
Quantum Clustering is a powerful method to detect clusters in data with mixed
density. However, it is very sensitive to a length parameter that is inherent
to the Schr\"odinger equation. In addition, linking data points into clusters
requires local estimates of covariance that are also controlled by length
parameters. This raises the question of how to adjust the control parameters of
the Schr\"odinger equation for optimal clustering. We propose a probabilistic
framework that provides an objective function for the goodness-of-fit to the
data, enabling the control parameters to be optimised within a Bayesian
framework. This naturally yields probabilities of cluster membership and data
partitions with specific numbers of clusters. The proposed framework is tested
on real and synthetic data sets, assessing its validity by measuring
concordance with known data structure by means of the Jaccard score (JS). This
work also proposes an objective way to measure performance in unsupervised
learning that correlates very well with JS. | [
"stat.ML",
"cs.LG"
] |
We propose a reinforcement learning approach for real-time exposure control
of a mobile camera that is personalizable. Our approach is based on Markov
Decision Process (MDP). In the camera viewfinder or live preview mode, given
the current frame, our system predicts the change in exposure so as to optimize
the trade-off among image quality, fast convergence, and minimal temporal
oscillation. We model the exposure prediction function as a fully convolutional
neural network that can be trained through Gaussian policy gradient in an
end-to-end fashion. As a result, our system can associate scene semantics with
exposure values; it can also be extended to personalize the exposure
adjustments for a user and device. We improve the learning performance by
incorporating an adaptive metering module that links semantics with exposure.
This adaptive metering module generalizes the conventional spot or matrix
metering techniques. We validate our system using the MIT FiveK and our own
datasets captured using iPhone 7 and Google Pixel. Experimental results show
that our system exhibits stable real-time behavior while improving visual
quality compared to what is achieved through native camera control. | [
"cs.CV"
] |
Due to confidentiality issues, it can be difficult to access or share
interesting datasets for methodological development in actuarial science, or
other fields where personal data are important. We show how to design three
different types of generative adversarial networks (GANs) that can build a
synthetic insurance dataset from a confidential original dataset. The goal is
to obtain synthetic data that no longer contains sensitive information but
still has the same structure as the original dataset and retains the
multivariate relationships. In order to adequately model the specific
characteristics of insurance data, we use GAN architectures adapted for
multi-categorical data: a Wassertein GAN with gradient penalty (MC-WGAN-GP), a
conditional tabular GAN (CTGAN) and a Mixed Numerical and Categorical
Differentially Private GAN (MNCDP-GAN). For transparency, the approaches are
illustrated using a public dataset, the French motor third party liability
data. We compare the three different GANs on various aspects: ability to
reproduce the original data structure and predictive models, privacy, and ease
of use. We find that the MC-WGAN-GP synthesizes the best data, the CTGAN is the
easiest to use, and the MNCDP-GAN guarantees differential privacy. | [
"stat.ML",
"cs.LG"
] |
Deep representation learning using triplet network for classification suffers
from a lack of theoretical foundation and difficulty in tuning both the network
and classifiers for performance. To address the problem, local-margin triplet
loss along with local positive and negative mining strategy is proposed with
theory on how the strategy integrate nearest-neighbor hyper-parameter with
triplet learning to increase subsequent classification performance. Results in
experiments with 2 public datasets, MNIST and Cifar-10, and 2 small medical
image datasets demonstrate that proposed strategy outperforms end-to-end
softmax and typical triplet loss in settings without data augmentation while
maintaining utility of transferable feature for related tasks. The method
serves as a good performance baseline where end-to-end methods encounter
difficulties such as small sample data with limited allowable data
augmentation. | [
"cs.CV",
"cs.LG"
] |
A key topic in classification is the accuracy loss produced when the data
distribution in the training (source) domain differs from that in the testing
(target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and
object category recognition. In this paper, we present a novel domain
adaptation method that leverages multiple target domains (or sub-domains) in a
hierarchical adaptation tree. The core idea is to exploit the commonalities and
differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea
to the adaptive SSVM (A-SSVM), which only requires the target domain samples
together with the existing source-domain classifier for performing the desired
adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection and object
category recognition. In the former we apply HA-SSVM to the deformable
part-based model (DPM) while in the latter HA-SSVM is applied to multi-category
classifiers. In both cases, we show how HA-SSVM is effective in increasing the
detection/recognition accuracy with respect to adaptation strategies that
ignore the structure of the target data. Since, the sub-domains of the target
data are not always known a priori, we shown how HA-SSVM can incorporate
sub-domain structure discovery for object category recognition. | [
"cs.CV",
"cs.LG"
] |
Humans can envision a realistic photo given a free-hand sketch that is not
only spatially imprecise and geometrically distorted but also without colors
and visual details. We study unsupervised sketch-to-photo synthesis for the
first time, learning from unpaired sketch-photo data where the target photo for
a sketch is unknown during training. Existing works only deal with style change
or spatial deformation alone, synthesizing photos from edge-aligned line
drawings or transforming shapes within the same modality, e.g., color images.
Our key insight is to decompose unsupervised sketch-to-photo synthesis into a
two-stage translation task: First shape translation from sketches to grayscale
photos and then content enrichment from grayscale to color photos. We also
incorporate a self-supervised denoising objective and an attention module to
handle abstraction and style variations that are inherent and specific to
sketches. Our synthesis is sketch-faithful and photo-realistic to enable
sketch-based image retrieval in practice. An exciting corollary product is a
universal and promising sketch generator that captures human visual perception
beyond the edge map of a photo. | [
"cs.CV"
] |
This paper introduces an approach to Reinforcement Learning Algorithm by
comparing their immediate rewards using a variation of Q-Learning algorithm.
Unlike the conventional Q-Learning, the proposed algorithm compares current
reward with immediate reward of past move and work accordingly. Relative reward
based Q-learning is an approach towards interactive learning. Q-Learning is a
model free reinforcement learning method that used to learn the agents. It is
observed that under normal circumstances algorithm take more episodes to reach
optimal Q-value due to its normal reward or sometime negative reward. In this
new form of algorithm agents select only those actions which have a higher
immediate reward signal in comparison to previous one. The contribution of this
article is the presentation of new Q-Learning Algorithm in order to maximize
the performance of algorithm and reduce the number of episode required to reach
optimal Q-value. Effectiveness of proposed algorithm is simulated in a 20 x20
Grid world deterministic environment and the result for the two forms of
Q-Learning Algorithms is given. | [
"cs.LG"
] |
Generative adversarial networks (GANs) learn the distribution of observed
samples through a zero-sum game between two machine players, a generator and a
discriminator. While GANs achieve great success in learning the complex
distribution of image, sound, and text data, they perform suboptimally in
learning multi-modal distribution-learning benchmarks including Gaussian
mixture models (GMMs). In this paper, we propose Generative Adversarial
Training for Gaussian Mixture Models (GAT-GMM), a minimax GAN framework for
learning GMMs. Motivated by optimal transport theory, we design the zero-sum
game in GAT-GMM using a random linear generator and a softmax-based quadratic
discriminator architecture, which leads to a non-convex concave minimax
optimization problem. We show that a Gradient Descent Ascent (GDA) method
converges to an approximate stationary minimax point of the GAT-GMM
optimization problem. In the benchmark case of a mixture of two symmetric,
well-separated Gaussians, we further show this stationary point recovers the
true parameters of the underlying GMM. We numerically support our theoretical
findings by performing several experiments, which demonstrate that GAT-GMM can
perform as well as the expectation-maximization algorithm in learning mixtures
of two Gaussians. | [
"cs.LG",
"stat.ML"
] |
Human activity recognition (HAR) by wearable sensor devices embedded in the
Internet of things (IOT) can play a significant role in remote health
monitoring and emergency notification, to provide healthcare of higher
standards. The purpose of this study is to investigate a human activity
recognition method of accrued decision accuracy and speed of execution to be
applicable in healthcare. This method classifies wearable sensor acceleration
time series data of human movement using efficient classifier combination of
feature engineering-based and feature learning-based data representation.
Leave-one-subject-out cross-validation of the method with data acquired from 44
subjects wearing a single waist-worn accelerometer on a smart textile, and
engaged in a variety of 10 activities, yields an average recognition rate of
90%, performing significantly better than individual classifiers. The method
easily accommodates functional and computational parallelization to bring
execution time significantly down. | [
"cs.LG"
] |
Surgical instrument segmentation is a key component in developing
context-aware operating rooms. Existing works on this task heavily rely on the
supervision of a large amount of labeled data, which involve laborious and
expensive human efforts. In contrast, a more affordable unsupervised approach
is developed in this paper. To train our model, we first generate anchors as
pseudo labels for instruments and background tissues respectively by fusing
coarse handcrafted cues. Then a semantic diffusion loss is proposed to resolve
the ambiguity in the generated anchors via the feature correlation between
adjacent video frames. In the experiments on the binary instrument segmentation
task of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge
dataset, the proposed method achieves 0.71 IoU and 0.81 Dice score without
using a single manual annotation, which is promising to show the potential of
unsupervised learning for surgical tool segmentation. | [
"cs.CV"
] |
Missing data is an inevitable and ubiquitous problem for traffic data
collection in intelligent transportation systems. Despite extensive research
regarding traffic data imputation, there still exist two limitations to be
addressed: first, existing approaches fail to capture the complex
spatiotemporal dependencies in traffic data, especially the dynamic spatial
dependencies evolving with time; second, prior studies mainly focus on randomly
missing patterns while other more complex missing scenarios are less discussed.
To fill these research gaps, we propose a novel deep learning framework called
Dynamic Spatiotemporal Graph Convolutional Neural Networks (DSTGCN) to impute
missing traffic data. The model combines the recurrent architecture with
graph-based convolutions to model the spatiotemporal dependencies. Moreover, we
introduce a graph structure estimation technique to model the dynamic spatial
dependencies from real-time traffic information and road network structure.
Extensive experiments based on two public traffic speed datasets are conducted
to compare our proposed model with state-of-the-art deep learning approaches in
four types of missing patterns. The results show that our proposed model
outperforms existing deep learning models in all kinds of missing scenarios and
the graph structure estimation technique contributes to the model performance.
We further compare our proposed model with a tensor factorization model and
find distinct behaviors across different model families under different
training schemes and data availability. | [
"cs.LG",
"cs.AI"
] |
Image Super Resolution (SR) finds applications in areas where images need to
be closely inspected by the observer to extract enhanced information. One such
focused application is an offline forensic analysis of surveillance feeds. Due
to the limitations of camera hardware, camera pose, limited bandwidth, varying
illumination conditions, and occlusions, the quality of the surveillance feed
is significantly degraded at times, thereby compromising monitoring of
behavior, activities, and other sporadic information in the scene. For the
proposed research work, we have inspected the effectiveness of four
conventional yet effective SR algorithms and three deep learning-based SR
algorithms to seek the finest method that executes well in a surveillance
environment with limited training data op-tions. These algorithms generate an
enhanced resolution output image from a sin-gle low-resolution (LR) input
image. For performance analysis, a subset of 220 images from six surveillance
datasets has been used, consisting of individuals with varying distances from
the camera, changing illumination conditions, and complex backgrounds. The
performance of these algorithms has been evaluated and compared using both
qualitative and quantitative metrics. These SR algo-rithms have also been
compared based on face detection accuracy. By analyzing and comparing the
performance of all the algorithms, a Convolutional Neural Network (CNN) based
SR technique using an external dictionary proved to be best by achieving robust
face detection accuracy and scoring optimal quantitative metric results under
different surveillance conditions. This is because the CNN layers progressively
learn more complex features using an external dictionary. | [
"cs.CV"
] |
A popular paradigm for 3D point cloud registration is by extracting 3D
keypoint correspondences, then estimating the registration function from the
correspondences using a robust algorithm. However, many existing 3D keypoint
techniques tend to produce large proportions of erroneous correspondences or
outliers, which significantly increases the cost of robust estimation. An
alternative approach is to directly search for the subset of correspondences
that are pairwise consistent, without optimising the registration function.
This gives rise to the combinatorial problem of matching with pairwise
constraints. In this paper, we propose a very efficient maximum clique
algorithm to solve matching with pairwise constraints. Our technique combines
tree searching with efficient bounding and pruning based on graph colouring. We
demonstrate that, despite the theoretical intractability, many real problem
instances can be solved exactly and quickly (seconds to minutes) with our
algorithm, which makes our approach an excellent alternative to standard robust
techniques for 3D registration. | [
"cs.CV",
"I.4"
] |
In this era of digital information explosion, an abundance of data from
numerous modalities is being generated as well as archived everyday. However,
most problems associated with training Deep Neural Networks still revolve
around lack of data that is rich enough for a given task. Data is required not
only for training an initial model, but also for future learning tasks such as
Model Compression and Incremental Learning. A diverse dataset may be used for
training an initial model, but it may not be feasible to store it throughout
the product life cycle due to data privacy issues or memory constraints. We
propose to bridge the gap between the abundance of available data and lack of
relevant data, for the future learning tasks of a given trained network. We use
the available data, that may be an imbalanced subset of the original training
dataset, or a related domain dataset, to retrieve representative samples from a
trained classifier, using a novel Data-enriching GAN (DeGAN) framework. We
demonstrate that data from a related domain can be leveraged to achieve
state-of-the-art performance for the tasks of Data-free Knowledge Distillation
and Incremental Learning on benchmark datasets. We further demonstrate that our
proposed framework can enrich any data, even from unrelated domains, to make it
more useful for the future learning tasks of a given network. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Deep Learning (DL) methods show very good performance when trained on large,
balanced data sets. However, many practical problems involve imbalanced data
sets, or/and classes with a small number of training samples. The performance
of DL methods as well as more traditional classifiers drops significantly in
such settings. Most of the existing solutions for imbalanced problems focus on
customizing the data for training. A more principled solution is to use mixed
Hinge-Minimax risk [19] specifically designed to solve binary problems with
imbalanced training sets. Here we propose a Latent Hinge Minimax (LHM) risk and
a training algorithm that generalizes this paradigm to an ensemble of
hyperplanes that can form arbitrary complex, piecewise linear boundaries. To
extract good features, we combine LHM model with CNN via transfer learning. To
solve multi-class problem we map pre-trained category-specific LHM classifiers
to a multi-class neural network and adjust the weights with very fast tuning.
LHM classifier enables the use of unlabeled data in its training and the
mapping allows for multi-class inference, resulting in a classifier that
performs better than alternatives when trained on a small number of training
samples. | [
"cs.LG",
"cs.CV"
] |
Recent advances in time series classification have largely focused on methods
that either employ deep learning or utilize other machine learning models for
feature extraction. Though successful, their power often comes at the
requirement of computational complexity. In this paper, we introduce GeoStat
representations for time series. GeoStat representations are based off of a
generalization of recent methods for trajectory classification, and summarize
the information of a time series in terms of comprehensive statistics of
(possibly windowed) distributions of easy to compute differential geometric
quantities, requiring no dynamic time warping. The features used are intuitive
and require minimal parameter tuning. We perform an exhaustive evaluation of
GeoStat on a number of real datasets, showing that simple KNN and SVM
classifiers trained on these representations exhibit surprising performance
relative to modern single model methods requiring significant computational
power, achieving state of the art results in many cases. In particular, we show
that this methodology achieves good performance on a challenging dataset
involving the classification of fishing vessels, where our methods achieve good
performance relative to the state of the art despite only having access to
approximately two percent of the dataset used in training and evaluating this
state of the art. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Detecting and localizing objects in the real 3D space, which plays a crucial
role in scene understanding, is particularly challenging given only a monocular
image due to the geometric information loss during imagery projection. We
propose MonoGRNet for the amodal 3D object detection from a monocular image via
geometric reasoning in both the observed 2D projection and the unobserved depth
dimension. MonoGRNet decomposes the monocular 3D object detection task into
four sub-tasks including 2D object detection, instance-level depth estimation,
projected 3D center estimation and local corner regression. The task
decomposition significantly facilitates the monocular 3D object detection,
allowing the target 3D bounding boxes to be efficiently predicted in a single
forward pass, without using object proposals, post-processing or the
computationally expensive pixel-level depth estimation utilized by previous
methods. In addition, MonoGRNet flexibly adapts to both fully and weakly
supervised learning, which improves the feasibility of our framework in diverse
settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets.
Results demonstrate the promising performance of our framework in various
scenarios. | [
"cs.CV"
] |
Learning disentanglement aims at finding a low dimensional representation
which consists of multiple explanatory and generative factors of the
observational data. The framework of variational autoencoder (VAE) is commonly
used to disentangle independent factors from observations. However, in real
scenarios, factors with semantics are not necessarily independent. Instead,
there might be an underlying causal structure which renders these factors
dependent. We thus propose a new VAE based framework named CausalVAE, which
includes a Causal Layer to transform independent exogenous factors into causal
endogenous ones that correspond to causally related concepts in data. We
further analyze the model identifiabitily, showing that the proposed model
learned from observations recovers the true one up to a certain degree.
Experiments are conducted on various datasets, including synthetic and real
word benchmark CelebA. Results show that the causal representations learned by
CausalVAE are semantically interpretable, and their causal relationship as a
Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we
demonstrate that the proposed CausalVAE model is able to generate
counterfactual data through "do-operation" to the causal factors. | [
"cs.LG",
"stat.ML"
] |
Human vision is able to capture the part-whole hierarchical information from
the entire scene. This paper presents the Visual Parser (ViP) that explicitly
constructs such a hierarchy with transformers. ViP divides visual
representations into two levels, the part level and the whole level.
Information of each part represents a combination of several independent
vectors within the whole. To model the representations of the two levels, we
first encode the information from the whole into part vectors through an
attention mechanism, then decode the global information within the part vectors
back into the whole representation. By iteratively parsing the two levels with
the proposed encoder-decoder interaction, the model can gradually refine the
features on both levels. Experimental results demonstrate that ViP can achieve
very competitive performance on three major tasks e.g. classification,
detection and instance segmentation. In particular, it can surpass the previous
state-of-the-art CNN backbones by a large margin on object detection. The tiny
model of the ViP family with $7.2\times$ fewer parameters and $10.9\times$
fewer FLOPS can perform comparably with the largest model
ResNeXt-101-64$\times$4d of ResNe(X)t family. Visualization results also
demonstrate that the learnt parts are highly informative of the predicting
class, making ViP more explainable than previous fundamental architectures.
Code is available at https://github.com/kevin-ssy/ViP. | [
"cs.CV"
] |
In this paper, we examine the long-neglected yet important effects of point
sampling patterns in point cloud GANs. Through extensive experiments, we show
that sampling-insensitive discriminators (e.g.PointNet-Max) produce shape point
clouds with point clustering artifacts while sampling-oversensitive
discriminators (e.g.PointNet++, DGCNN) fail to guide valid shape generation. We
propose the concept of sampling spectrum to depict the different sampling
sensitivities of discriminators. We further study how different evaluation
metrics weigh the sampling pattern against the geometry and propose several
perceptual metrics forming a sampling spectrum of metrics. Guided by the
proposed sampling spectrum, we discover a middle-point sampling-aware baseline
discriminator, PointNet-Mix, which improves all existing point cloud generators
by a large margin on sampling-related metrics. We point out that, though recent
research has been focused on the generator design, the main bottleneck of point
cloud GAN actually lies in the discriminator design. Our work provides both
suggestions and tools for building future discriminators. We will release the
code to facilitate future research. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Reinforcement learning (RL) has traditionally been understood from an
episodic perspective; the concept of non-episodic RL, where there is no restart
and therefore no reliable recovery, remains elusive. A fundamental question in
non-episodic RL is how to measure the performance of a learner and derive
algorithms to maximize such performance. Conventional wisdom is to maximize the
difference between the average reward received by the learner and the maximal
long-term average reward. In this paper, we argue that if the total time budget
is relatively limited compared to the complexity of the environment, such
comparison may fail to reflect the finite-time optimality of the learner. We
propose a family of measures, called $\gamma$-regret, which we believe to
better capture the finite-time optimality. We give motivations and derive lower
and upper bounds for such measures. Note: A follow-up work (arXiv:2010.00587)
has improved both our lower and upper bound, the gap is now closed at
$\tilde{\Theta}\left(\frac{\sqrt{SAT}}{(1 - \gamma)^{\frac{1}{2}}}\right)$. | [
"cs.LG",
"stat.ML"
] |
To see is to sketch -- free-hand sketching naturally builds ties between
human and machine vision. In this paper, we present a novel approach for
translating an object photo to a sketch, mimicking the human sketching process.
This is an extremely challenging task because the photo and sketch domains
differ significantly. Furthermore, human sketches exhibit various levels of
sophistication and abstraction even when depicting the same object instance in
a reference photo. This means that even if photo-sketch pairs are available,
they only provide weak supervision signal to learn a translation model.
Compared with existing supervised approaches that solve the problem of
D(E(photo)) -> sketch, where E($\cdot$) and D($\cdot$) denote encoder and
decoder respectively, we take advantage of the inverse problem (e.g.,
D(E(sketch)) -> photo), and combine with the unsupervised learning tasks of
within-domain reconstruction, all within a multi-task learning framework.
Compared with existing unsupervised approaches based on cycle consistency
(i.e., D(E(D(E(photo)))) -> photo), we introduce a shortcut consistency
enforced at the encoder bottleneck (e.g., D(E(photo)) -> photo) to exploit the
additional self-supervision. Both qualitative and quantitative results show
that the proposed model is superior to a number of state-of-the-art
alternatives. We also show that the synthetic sketches can be used to train a
better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively
alleviating the problem of sketch data scarcity. | [
"cs.CV"
] |
We present a method for hierarchical image segmentation that defines a
disaffinity graph on the image, over-segments it into watershed basins, defines
a new graph on the basins, and then merges basins with a modified,
size-dependent version of single linkage clustering. The quasilinear runtime of
the method makes it suitable for segmenting large images. We illustrate the
method on the challenging problem of segmenting 3D electron microscopic brain
images. | [
"cs.CV"
] |
We present an algorithm for supervised learning using tensor networks,
employing a step of preprocessing the data by coarse-graining through a
sequence of wavelet transformations. We represent these transformations as a
set of tensor network layers identical to those in a multi-scale entanglement
renormalization ansatz (MERA) tensor network, and perform supervised learning
and regression tasks through a model based on a matrix product state (MPS)
tensor network acting on the coarse-grained data. Because the entire model
consists of tensor contractions (apart from the initial non-linear feature
map), we can adaptively fine-grain the optimized MPS model backwards through
the layers with essentially no loss in performance. The MPS itself is trained
using an adaptive algorithm based on the density matrix renormalization group
(DMRG) algorithm. We test our methods by performing a classification task on
audio data and a regression task on temperature time-series data, studying the
dependence of training accuracy on the number of coarse-graining layers and
showing how fine-graining through the network may be used to initialize models
with access to finer-scale features. | [
"stat.ML",
"cs.LG",
"quant-ph"
] |
Generation of 3D data by deep neural network has been attracting increasing
attention in the research community. The majority of extant works resort to
regular representations such as volumetric grids or collection of images;
however, these representations obscure the natural invariance of 3D shapes
under geometric transformations and also suffer from a number of other issues.
In this paper we address the problem of 3D reconstruction from a single image,
generating a straight-forward form of output -- point cloud coordinates. Along
with this problem arises a unique and interesting issue, that the groundtruth
shape for an input image may be ambiguous. Driven by this unorthodox output
form and the inherent ambiguity in groundtruth, we design architecture, loss
function and learning paradigm that are novel and effective. Our final solution
is a conditional shape sampler, capable of predicting multiple plausible 3D
point clouds from an input image. In experiments not only can our system
outperform state-of-the-art methods on single image based 3d reconstruction
benchmarks; but it also shows a strong performance for 3d shape completion and
promising ability in making multiple plausible predictions. | [
"cs.CV"
] |
Objective: Medical relations are the core components of medical knowledge
graphs that are needed for healthcare artificial intelligence. However, the
requirement of expert annotation by conventional algorithm development
processes creates a major bottleneck for mining new relations. In this paper,
we present Hi-RES, a framework for high-throughput relation extraction
algorithm development. We also show that combining knowledge articles with
electronic health records (EHRs) significantly increases the classification
accuracy. Methods: We use relation triplets obtained from structured databases
and semistructured webpages to label sentences from target corpora as positive
training samples. Two methods are also provided for creating improved negative
samples by combining positive samples with na\"ive negative samples. We propose
a common model that summarizes sentence information using large-scale
pretrained language models and multi-instance attention, which then joins with
the concept embeddings trained from the EHRs for relation prediction. Results:
We apply the Hi-RES framework to develop classification algorithms for
disorder-disorder relations and disorder-location relations. Millions of
sentences are created as training data. Using pretrained language models and
EHR-based embeddings individually provides considerable accuracy increases over
those of previous models. Joining them together further tremendously increases
the accuracy to 0.947 and 0.998 for the two sets of relations, respectively,
which are 10-17 percentage points higher than those of previous models.
Conclusion: Hi-RES is an efficient framework for achieving high-throughput and
accurate relation extraction algorithm development. | [
"cs.LG",
"stat.ML"
] |
Rationalizing which parts of a molecule drive the predictions of a molecular
graph convolutional neural network (GCNN) can be difficult. To help, we propose
two simple regularization techniques to apply during the training of GCNNs:
Batch Representation Orthonormalization (BRO) and Gini regularization. BRO,
inspired by molecular orbital theory, encourages graph convolution operations
to generate orthonormal node embeddings. Gini regularization is applied to the
weights of the output layer and constrains the number of dimensions the model
can use to make predictions. We show that Gini and BRO regularization can
improve the accuracy of state-of-the-art GCNN attribution methods on artificial
benchmark datasets. In a real-world setting, we demonstrate that medicinal
chemists significantly prefer explanations extracted from regularized models.
While we only study these regularizers in the context of GCNNs, both can be
applied to other types of neural networks | [
"stat.ML",
"cs.LG"
] |
In reinforcement learning, robust policies for high-stakes decision-making
problems with limited data are usually computed by optimizing the percentile
criterion, which minimizes the probability of a catastrophic failure.
Unfortunately, such policies are typically overly conservative as the
percentile criterion is non-convex, difficult to optimize, and ignores the mean
performance. To overcome these shortcomings, we study the soft-robust
criterion, which uses risk measures to balance the mean and percentile
criterion better. In this paper, we establish the soft-robust criterion's
fundamental properties, show that it is NP-hard to optimize, and propose and
analyze two algorithms to approximately optimize it. Our theoretical analyses
and empirical evaluations demonstrate that our algorithms compute much less
conservative solutions than the existing approximate methods for optimizing the
percentile-criterion. | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] |
Symmetry transformations induce invariances which are frequently described
with deep latent variable models. In many complex domains, such as the chemical
space, invariances can be observed, yet the corresponding symmetry
transformation cannot be formulated analytically. We propose to learn the
symmetry transformation with a model consisting of two latent subspaces, where
the first subspace captures the target and the second subspace the remaining
invariant information. Our approach is based on the deep information bottleneck
in combination with a continuous mutual information regulariser. Unlike
previous methods, we focus on the challenging task of minimising mutual
information in continuous domains. To this end, we base the calculation of
mutual information on correlation matrices in combination with a bijective
variable transformation. Extensive experiments demonstrate that our model
outperforms state-of-the-art methods on artificial and molecular datasets. | [
"cs.LG",
"stat.ML"
] |
Deep reinforcement learning has achieved great successes in recent years,
however, one main challenge is the sample inefficiency. In this paper, we focus
on how to use action guidance by means of a non-expert demonstrator to improve
sample efficiency in a domain with sparse, delayed, and possibly deceptive
rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a
new framework where even a non-expert simulated demonstrator, e.g., planning
algorithms such as Monte Carlo tree search with a small number rollouts, can be
integrated within asynchronous distributed deep reinforcement learning methods.
Compared to a vanilla deep RL algorithm, our proposed methods both learn faster
and converge to better policies on a two-player mini version of the Pommerman
game. | [
"cs.LG",
"cs.MA",
"stat.ML"
] |
We propose an end-to-end variational generative model for scene layout
synthesis conditioned on scene graphs. Unlike unconditional scene layout
generation, we use scene graphs as an abstract but general representation to
guide the synthesis of diverse scene layouts that satisfy relationships
included in the scene graph. This gives rise to more flexible control over the
synthesis process, allowing various forms of inputs such as scene layouts
extracted from sentences or inferred from a single color image. Using our
conditional layout synthesizer, we can generate various layouts that share the
same structure of the input example. In addition to this conditional generation
design, we also integrate a differentiable rendering module that enables layout
refinement using only 2D projections of the scene. Given a depth and a
semantics map, the differentiable rendering module enables optimizing over the
synthesized layout to fit the given input in an analysis-by-synthesis fashion.
Experiments suggest that our model achieves higher accuracy and diversity in
conditional scene synthesis and allows exemplar-based scene generation from
various input forms. | [
"cs.CV"
] |
Nowadays, deep learning is widely applied to extract features for similarity
computation in person re-identification (re-ID) and have achieved great
success. However, due to the non-overlapping between training and testing IDs,
the difference between the data used for model training and the testing data
makes the performance of learned feature degraded during testing. Hence,
re-ranking is proposed to mitigate this issue and various algorithms have been
developed. However, most of existing re-ranking methods focus on replacing the
Euclidean distance with sophisticated distance metrics, which are not friendly
to downstream tasks and hard to be used for fast retrieval of massive data in
real applications. In this work, we propose a graph-based re-ranking method to
improve learned features while still keeping Euclidean distance as the
similarity metric. Inspired by graph convolution networks, we develop an
operator to propagate features over an appropriate graph. Since graph is the
essential key for the propagation, two important criteria are considered for
designing the graph, and three different graphs are explored accordingly.
Furthermore, a simple yet effective method is proposed to generate a profile
vector for each tracklet in videos, which helps extend our method to video
re-ID. Extensive experiments on three benchmark data sets, e.g., Market-1501,
Duke, and MARS, demonstrate the effectiveness of our proposed approach. | [
"cs.CV"
] |
Knowledge distillation (KD) has become an important technique for model
compression and knowledge transfer. In this work, we first perform a
comprehensive analysis of the knowledge transferred by different KD methods. We
demonstrate that traditional KD methods, which minimize the KL divergence of
softmax outputs between networks, are related to the knowledge alignment of an
individual sample only. Meanwhile, recent contrastive learning-based KD methods
mainly transfer relational knowledge between different samples, namely,
knowledge correlation. While it is important to transfer the full knowledge
from teacher to student, we introduce the Multi-level Knowledge Distillation
(MLKD) by effectively considering both knowledge alignment and correlation.
MLKD is task-agnostic and model-agnostic, and can easily transfer knowledge
from supervised or self-supervised pretrained teachers. We show that MLKD can
improve the reliability and transferability of learned representations.
Experiments demonstrate that MLKD outperforms other state-of-the-art methods on
a large number of experimental settings including different (a) pretraining
strategies (b) network architectures (c) datasets (d) tasks. | [
"cs.CV"
] |
Recent advances on 3D object detection heavily rely on how the 3D data are
represented, \emph{i.e.}, voxel-based or point-based representation. Many
existing high performance 3D detectors are point-based because this structure
can better retain precise point positions. Nevertheless, point-level features
lead to high computation overheads due to unordered storage. In contrast, the
voxel-based structure is better suited for feature extraction but often yields
lower accuracy because the input data are divided into grids. In this paper, we
take a slightly different viewpoint -- we find that precise positioning of raw
points is not essential for high performance 3D object detection and that the
coarse voxel granularity can also offer sufficient detection accuracy. Bearing
this view in mind, we devise a simple but effective voxel-based framework,
named Voxel R-CNN. By taking full advantage of voxel features in a two stage
approach, our method achieves comparable detection accuracy with
state-of-the-art point-based models, but at a fraction of the computation cost.
Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region
Proposal Network and a detect head. A voxel RoI pooling is devised to extract
RoI features directly from voxel features for further refinement. Extensive
experiments are conducted on the widely used KITTI Dataset and the more recent
Waymo Open Dataset. Our results show that compared to existing voxel-based
methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a
real-time frame processing rate, \emph{i.e}., at a speed of 25 FPS on an NVIDIA
RTX 2080 Ti GPU. The code is available at
\url{https://github.com/djiajunustc/Voxel-R-CNN}. | [
"cs.CV"
] |
Machine learning on graphs has been extensively studied in both academic and
industry. However, as the literature on graph learning booms with a vast number
of emerging methods and techniques, it becomes increasingly difficult to
manually design the optimal machine learning algorithm for different
graph-related tasks. To solve this critical challenge, automated machine
learning (AutoML) on graphs which combines the strength of graph machine
learning and AutoML together, is gaining attention from the research community.
Therefore, we comprehensively survey AutoML on graphs in this paper, primarily
focusing on hyper-parameter optimization (HPO) and neural architecture search
(NAS) for graph machine learning. We further overview libraries related to
automated graph machine learning and in-depth discuss AutoGL, the first
dedicated open-source library for AutoML on graphs. In the end, we share our
insights on future research directions for automated graph machine learning.
This paper is the first systematic and comprehensive review of automated
machine learning on graphs to the best of our knowledge. | [
"cs.LG"
] |
Vision Transformers (ViT) have been shown to attain highly competitive
performance for a wide range of vision applications, such as image
classification, object detection and semantic image segmentation. In comparison
to convolutional neural networks, the Vision Transformer's weaker inductive
bias is generally found to cause an increased reliance on model regularization
or data augmentation (``AugReg'' for short) when training on smaller training
datasets. We conduct a systematic empirical study in order to better understand
the interplay between the amount of training data, AugReg, model size and
compute budget. As one result of this study we find that the combination of
increased compute and AugReg can yield models with the same performance as
models trained on an order of magnitude more training data: we train ViT models
of various sizes on the public ImageNet-21k dataset which either match or
outperform their counterparts trained on the larger, but not publicly available
JFT-300M dataset. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Certain facial parts are salient (unique) in appearance, which substantially
contribute to the holistic recognition of a subject. Occlusion of these salient
parts deteriorates the performance of face recognition algorithms. In this
paper, we propose a generative model to reconstruct the missing parts of the
face which are under occlusion. The proposed generative model (SD-GAN)
reconstructs a face preserving the illumination variation and identity of the
face. A novel adversarial training algorithm has been designed for a bimodal
mutually exclusive Generative Adversarial Network (GAN) model, for faster
convergence. A novel adversarial "structural" loss function is also proposed,
comprising of two components: a holistic and a local loss, characterized by
SSIM and patch-wise MSE. Ablation studies on real and synthetically occluded
face datasets reveal that our proposed technique outperforms the competing
methods by a considerable margin, even for boosting the performance of Face
Recognition. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
With autonomous driving developing in a booming stage, accurate object
detection in complex scenarios attract wide attention to ensure the safety of
autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a
mainstream solution for accurate obstacle detection. This article presents a
detailed survey on mmWave radar and vision fusion based obstacle detection
methods. Firstly, we introduce the tasks, evaluation criteria and datasets of
object detection for autonomous driving. Then, the process of mmWave radar and
vision fusion is divided into three parts: sensor deployment, sensor
calibration and sensor fusion, which are reviewed comprehensively. Especially,
we classify the fusion methods into data level, decision level and feature
level fusion methods. Besides, we introduce the fusion of lidar and vision in
autonomous driving in the aspects of obstacle detection, object classification
and road segmentation, which is promising in the future. Finally, we summarize
this article. | [
"cs.CV"
] |
This paper attempts at improving the accuracy of Human Action Recognition
(HAR) by fusion of depth and inertial sensor data. Firstly, we transform the
depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained
AlexNet on these images. Then, inertial data is converted into Signal Images
(SI) and another convolutional neural network (CNN) is trained on these images.
Finally, learned features are extracted from both CNN, fused together to make a
shared feature layer, and these features are fed to the classifier. We
experiment with two classifiers, namely Support Vector Machines (SVM) and
softmax classifier and compare their performances. The recognition accuracies
of each modality, depth data alone and sensor data alone are also calculated
and compared with fusion based accuracies to highlight the fact that fusion of
modalities yields better results than individual modalities. Experimental
results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves
state of the art results when compared to other recently proposed
visual-inertial action recognition methods. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
While several convolution-like operators have recently been proposed for
extracting features out of point clouds, down-sampling an unordered point cloud
in a deep neural network has not been rigorously studied. Existing methods
down-sample the points regardless of their importance for the output. As a
result, some important points in the point cloud may be removed, while less
valuable points may be passed to the next layers. In contrast, adaptive
down-sampling methods sample the points by taking into account the importance
of each point, which varies based on the application, task and training data.
In this paper, we propose a permutation-invariant learning-based adaptive
down-sampling layer, called Critical Points Layer (CPL), which reduces the
number of points in an unordered point cloud while retaining the important
points. Unlike most graph-based point cloud down-sampling methods that use
$k$-NN search algorithm to find the neighbouring points, CPL is a global
down-sampling method, rendering it computationally very efficient. The proposed
layer can be used along with any graph-based point cloud convolution layer to
form a convolutional neural network, dubbed CP-Net in this paper. We introduce
a CP-Net for $3$D object classification that achieves the best accuracy for the
ModelNet$40$ dataset among point cloud-based methods, which validates the
effectiveness of the CPL. | [
"cs.CV"
] |
When training a deep neural network for image classification, one can broadly
distinguish between two types of latent features of images that will drive the
classification. We can divide latent features into (i) "core" or "conditionally
invariant" features $X^\text{core}$ whose distribution $X^\text{core}\vert Y$,
conditional on the class $Y$, does not change substantially across domains and
(ii) "style" features $X^{\text{style}}$ whose distribution $X^{\text{style}}
\vert Y$ can change substantially across domains. Examples for style features
include position, rotation, image quality or brightness but also more complex
ones like hair color, image quality or posture for images of persons. Our goal
is to minimize a loss that is robust under changes in the distribution of these
style features. In contrast to previous work, we assume that the domain itself
is not observed and hence a latent variable.
We do assume that we can sometimes observe a typically discrete identifier or
"$\mathrm{ID}$ variable". In some applications we know, for example, that two
images show the same person, and $\mathrm{ID}$ then refers to the identity of
the person. The proposed method requires only a small fraction of images to
have $\mathrm{ID}$ information. We group observations if they share the same
class and identifier $(Y,\mathrm{ID})=(y,\mathrm{id})$ and penalize the
conditional variance of the prediction or the loss if we condition on
$(Y,\mathrm{ID})$. Using a causal framework, this conditional variance
regularization (CoRe) is shown to protect asymptotically against shifts in the
distribution of the style variables. Empirically, we show that the CoRe penalty
improves predictive accuracy substantially in settings where domain changes
occur in terms of image quality, brightness and color while we also look at
more complex changes such as changes in movement and posture. | [
"stat.ML",
"cs.LG"
] |
Efficient modeling of relational data arising in physical, social, and
information sciences is challenging due to complicated dependencies within the
data. In this work, we build off of semi-implicit graph variational
auto-encoders to capture higher-order statistics in a low-dimensional graph
latent representation. We incorporate hyperbolic geometry in the latent space
through a Poincare embedding to efficiently represent graphs exhibiting
hierarchical structure. To address the naive posterior latent distribution
assumptions in classical variational inference, we use semi-implicit
hierarchical variational Bayes to implicitly capture posteriors of given graph
data, which may exhibit heavy tails, multiple modes, skewness, and highly
correlated latent structures. We show that the existing semi-implicit
variational inference objective provably reduces information in the observed
graph. Based on this observation, we estimate and add an additional mutual
information term to the semi-implicit variational inference learning objective
to capture rich correlations arising between the input and latent spaces. We
show that the inclusion of this regularization term in conjunction with the
Poincare embedding boosts the quality of learned high-level representations and
enables more flexible and faithful graphical modeling. We experimentally
demonstrate that our approach outperforms existing graph variational
auto-encoders both in Euclidean and in hyperbolic spaces for edge link
prediction and node classification. | [
"cs.LG",
"stat.ML"
] |
Some data analysis applications comprise datasets, where explanatory
variables are expensive or tedious to acquire, but auxiliary data are readily
available and might help to construct an insightful training set. An example is
neuroimaging research on mental disorders, specifically learning a
diagnosis/prognosis model based on variables derived from expensive Magnetic
Resonance Imaging (MRI) scans, which often requires large sample sizes.
Auxiliary data, such as demographics, might help in selecting a smaller sample
that comprises the individuals with the most informative MRI scans. In active
learning literature, this problem has not yet been studied, despite promising
results in related problem settings that concern the selection of instances or
instance-feature pairs.
Therefore, we formulate this complementary problem of Active Selection of
Classification Features (ASCF): Given a primary task, which requires to learn a
model f: x-> y to explain/predict the relationship between an
expensive-to-acquire set of variables x and a class label y. Then, the
ASCF-task is to use a set of readily available selection variables z to select
these instances, that will improve the primary task's performance most when
acquiring their expensive features z and including them to the primary training
set.
We propose two utility-based approaches for this problem, and evaluate their
performance on three public real-world benchmark datasets. In addition, we
illustrate the use of these approaches to efficiently acquire MRI scans in the
context of neuroimaging research on mental disorders, based on a simulated
study design with real MRI data. | [
"cs.LG"
] |
We present Deep Graph Infomax (DGI), a general approach for learning node
representations within graph-structured data in an unsupervised manner. DGI
relies on maximizing mutual information between patch representations and
corresponding high-level summaries of graphs---both derived using established
graph convolutional network architectures. The learnt patch representations
summarize subgraphs centered around nodes of interest, and can thus be reused
for downstream node-wise learning tasks. In contrast to most prior approaches
to unsupervised learning with GCNs, DGI does not rely on random walk
objectives, and is readily applicable to both transductive and inductive
learning setups. We demonstrate competitive performance on a variety of node
classification benchmarks, which at times even exceeds the performance of
supervised learning. | [
"stat.ML",
"cs.IT",
"cs.LG",
"cs.SI",
"math.IT"
] |
Recent proposal of Wasserstein Index Generation model (WIG) has shown a new
direction for automatically generating indices. However, it is challenging in
practice to fit large datasets for two reasons. First, the Sinkhorn distance is
notoriously expensive to compute and suffers from dimensionality severely.
Second, it requires to compute a full $N\times N$ matrix to be fit into memory,
where $N$ is the dimension of vocabulary. When the dimensionality is too large,
it is even impossible to compute at all. I hereby propose a Lasso-based
shrinkage method to reduce dimensionality for the vocabulary as a
pre-processing step prior to fitting the WIG model. After we get the word
embedding from Word2Vec model, we could cluster these high-dimensional vectors
by $k$-means clustering, and pick most frequent tokens within each cluster to
form the "base vocabulary". Non-base tokens are then regressed on the vectors
of base token to get a transformation weight and we could thus represent the
whole vocabulary by only the "base tokens". This variant, called pruned WIG
(pWIG), will enable us to shrink vocabulary dimension at will but could still
achieve high accuracy. I also provide a \textit{wigpy} module in Python to
carry out computation in both flavor. Application to Economic Policy
Uncertainty (EPU) index is showcased as comparison with existing methods of
generating time-series sentiment indices. | [
"cs.LG",
"cs.CL",
"econ.GN",
"q-fin.EC"
] |
Given the ever-increasing computational costs of modern machine learning
models, we need to find new ways to reuse such expert models and thus tap into
the resources that have been invested in their creation. Recent work suggests
that the power of these massive models is captured by the representations they
learn. Therefore, we seek a model that can relate between different existing
representations and propose to solve this task with a conditionally invertible
network. This network demonstrates its capability by (i) providing generic
transfer between diverse domains, (ii) enabling controlled content synthesis by
allowing modification in other domains, and (iii) facilitating diagnosis of
existing representations by translating them into interpretable domains such as
images. Our domain transfer network can translate between fixed representations
without having to learn or finetune them. This allows users to utilize various
existing domain-specific expert models from the literature that had been
trained with extensive computational resources. Experiments on diverse
conditional image synthesis tasks, competitive image modification results and
experiments on image-to-image and text-to-image generation demonstrate the
generic applicability of our approach. For example, we translate between BERT
and BigGAN, state-of-the-art text and image models to provide text-to-image
generation, which neither of both experts can perform on their own. | [
"cs.CV",
"cs.LG"
] |
In this paper, we employ variational arguments to establish a connection
between ensemble methods for Neural Networks and Bayesian inference. We
consider an ensemble-based scheme where each model/particle corresponds to a
perturbation of the data by means of parametric bootstrap and a perturbation of
the prior. We derive conditions under which any optimization steps of the
particles makes the associated distribution reduce its divergence to the
posterior over model parameters. Such conditions do not require any particular
form for the approximation and they are purely geometrical, giving insights on
the behavior of the ensemble on a number of interesting models such as Neural
Networks with ReLU activations. Experiments confirm that ensemble methods can
be a valid alternative to approximate Bayesian inference; the theoretical
developments in the paper seek to explain this behavior. | [
"cs.LG",
"stat.ML"
] |
Recent CNN based object detectors, no matter one-stage methods like YOLO,
SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are
usually trying to directly finetune from ImageNet pre-trained models designed
for image classification. There has been little work discussing on the backbone
feature extractor specifically designed for the object detection. More
importantly, there are several differences between the tasks of image
classification and object detection. 1. Recent object detectors like FPN and
RetinaNet usually involve extra stages against the task of image classification
to handle the objects with various scales. 2. Object detection not only needs
to recognize the category of the object instances but also spatially locate the
position. Large downsampling factor brings large valid receptive field, which
is good for image classification but compromises the object location ability.
Due to the gap between the image classification and object detection, we
propose DetNet in this paper, which is a novel backbone network specifically
designed for object detection. Moreover, DetNet includes the extra stages
against traditional backbone network for image classification, while maintains
high spatial resolution in deeper layers. Without any bells and whistles,
state-of-the-art results have been obtained for both object detection and
instance segmentation on the MSCOCO benchmark based on our DetNet~(4.8G FLOPs)
backbone. The code will be released for the reproduction. | [
"cs.CV"
] |
Model explanations based on pure observational data cannot compute the
effects of features reliably, due to their inability to estimate how each
factor alteration could affect the rest. We argue that explanations should be
based on the causal model of the data and the derived intervened causal models,
that represent the data distribution subject to interventions. With these
models, we can compute counterfactuals, new samples that will inform us how the
model reacts to feature changes on our input. We propose a novel explanation
methodology based on Causal Counterfactuals and identify the limitations of
current Image Generative Models in their application to counterfactual
creation. | [
"stat.ML",
"cs.LG",
"cs.NE"
] |
In the image processing pipeline of almost every digital camera there is a
part dedicated to computational color constancy i.e. to removing the influence
of illumination on the colors of the image scene. Some of the best known
illumination estimation methods are the so called statistics-based methods.
They are less accurate than the learning-based illumination estimation methods,
but they are faster and simpler to implement in embedded systems, which is one
of the reasons for their widespread usage. Although in the relevant literature
it often appears as if they require no training, this is not true because they
have parameter values that need to be fine-tuned in order to be more accurate.
In this paper it is first shown that the accuracy of statistics-based methods
reported in most papers was not obtained by means of the necessary
cross-validation, but by using the whole benchmark datasets for both training
and testing. After that the corrected results are given for the best known
benchmark datasets. Finally, the so called green stability assumption is
proposed that can be used to fine-tune the values of the parameters of the
statistics-based methods by using only non-calibrated images without known
ground-truth illumination. The obtained accuracy is practically the same as
when using calibrated training images, but the whole process is much faster.
The experimental results are presented and discussed. The source code is
available at http://www.fer.unizg.hr/ipg/resources/color_constancy/. | [
"cs.CV"
] |
Sketchformer is a novel transformer-based representation for encoding
free-hand sketches input in a vector form, i.e. as a sequence of strokes.
Sketchformer effectively addresses multiple tasks: sketch classification,
sketch based image retrieval (SBIR), and the reconstruction and interpolation
of sketches. We report several variants exploring continuous and tokenized
input representations, and contrast their performance. Our learned embedding,
driven by a dictionary learning tokenization scheme, yields state of the art
performance in classification and image retrieval tasks, when compared against
baseline representations driven by LSTM sequence to sequence architectures:
SketchRNN and derivatives. We show that sketch reconstruction and interpolation
are improved significantly by the Sketchformer embedding for complex sketches
with longer stroke sequences. | [
"cs.CV"
] |
Traditional generative models are limited to predicting sequences of terminal
tokens. However, ambiguities in the generation task may lead to incorrect
outputs. Towards addressing this, we introduce Grammformers, transformer-based
grammar-guided models that learn (without explicit supervision) to generate
sketches -- sequences of tokens with holes. Through reinforcement learning,
Grammformers learn to introduce holes avoiding the generation of incorrect
tokens where there is ambiguity in the target task.
We train Grammformers for statement-level source code completion, i.e., the
generation of code snippets given an ambiguous user intent, such as a partial
code context. We evaluate Grammformers on code completion for C# and Python and
show that it generates 10-50% more accurate sketches compared to traditional
generative models and 37-50% longer sketches compared to sketch-generating
baselines trained with similar techniques. | [
"cs.LG",
"cs.SE"
] |
The two underlying requirements of face age progression, i.e. aging accuracy
and identity permanence, are not well studied in the literature. This paper
presents a novel generative adversarial network based approach to address the
issues in a coupled manner. It separately models the constraints for the
intrinsic subject-specific characteristics and the age-specific facial changes
with respect to the elapsed time, ensuring that the generated faces present
desired aging effects while simultaneously keeping personalized properties
stable. To ensure photo-realistic facial details, high-level age-specific
features conveyed by the synthesized face are estimated by a pyramidal
adversarial discriminator at multiple scales, which simulates the aging effects
with finer details. Further, an adversarial learning scheme is introduced to
simultaneously train a single generator and multiple parallel discriminators,
resulting in smooth continuous face aging sequences. The proposed method is
applicable even in the presence of variations in pose, expression, makeup,
etc., achieving remarkably vivid aging effects. Quantitative evaluations by a
COTS face recognition system demonstrate that the target age distributions are
accurately recovered, and 99.88% and 99.98% age progressed faces can be
correctly verified at 0.001% FAR after age transformations of approximately 28
and 23 years elapsed time on the MORPH and CACD databases, respectively. Both
visual and quantitative assessments show that the approach advances the
state-of-the-art. | [
"cs.CV"
] |
We consider model-based reinforcement learning (MBRL) in 2-agent,
high-fidelity continuous control problems -- an important domain for robots
interacting with other agents in the same workspace. For non-trivial dynamical
systems, MBRL typically suffers from accumulating errors. Several recent
studies have addressed this problem by learning latent variable models for
trajectory segments and optimizing over behavior in the latent space. In this
work, we investigate whether this approach can be extended to 2-agent
competitive and cooperative settings. The fundamental challenge is how to learn
models that capture interactions between agents, yet are disentangled to allow
for optimization of each agent behavior separately. We propose such models
based on a disentangled variational auto-encoder, and demonstrate our approach
on a simulated 2-robot manipulation task, where one robot can either help or
distract the other. We show that our approach has better sample efficiency than
a strong model-free RL baseline, and can learn both cooperative and adversarial
behavior from the same data. | [
"cs.LG",
"stat.ML"
] |
Knowledge distillation (KD) is generally considered as a technique for
performing model compression and learned-label smoothing. However, in this
paper, we study and investigate the KD approach from a new perspective: we
study its efficacy in training a deeper network without any residual
connections. We find that in most of the cases, non-residual student networks
perform equally or better than their residual versions trained on raw data
without KD (baseline network). Surprisingly, in some cases, they surpass the
accuracy of baseline networks even with the inferior teachers. After a certain
depth of non-residual student network, the accuracy drop, coming from the
removal of residual connections, is substantial, and training with KD boosts
the accuracy of the student up to a great extent; however, it does not fully
recover the accuracy drop. Furthermore, we observe that the conventional
teacher-student view of KD is incomplete and does not adequately explain our
findings. We propose a novel interpretation of KD with the Trainee-Mentor
hypothesis, which provides a holistic view of KD. We also present two
viewpoints, loss landscape, and feature reuse, to explain the interplay between
residual connections and KD. We substantiate our claims through extensive
experiments on residual networks. | [
"cs.CV",
"I.5.1; I.5.1"
] |
Deep hashing methods have received much attention recently, which achieve
promising results by taking advantage of the strong representation power of
deep networks. However, most existing deep hashing methods learn a whole set of
hashing functions independently, while ignore the correlations between
different hashing functions that can promote the retrieval accuracy greatly.
Inspired by the sequential decision ability of deep reinforcement learning, we
propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Our proposed DRLIH approach models the hashing learning problem as a sequential
decision process, which learns each hashing function by correcting the errors
imposed by previous ones and promotes retrieval accuracy. To the best of our
knowledge, this is the first work to address hashing problem from deep
reinforcement learning perspective. The main contributions of our proposed
DRLIH approach can be summarized as follows: (1) We propose a deep
reinforcement learning hashing network. In the proposed network, we utilize
recurrent neural network (RNN) as agents to model the hashing functions, which
take actions of projecting images into binary codes sequentially, so that the
current hashing function learning can take previous hashing functions' error
into account. (2) We propose a sequential learning strategy based on proposed
DRLIH. We define the state as a tuple of internal features of RNN's hidden
layers and image features, which can reflect history decisions made by the
agents. We also propose an action group method to enhance the correlation of
hash functions in the same group. Experiments on three widely-used datasets
demonstrate the effectiveness of our proposed DRLIH approach. | [
"cs.CV"
] |
Adversarial perturbation of images, in which a source image is deliberately
modified with the intent of causing a classifier to misclassify the image,
provides important insight into the robustness of image classifiers. In this
work we develop two new methods for constructing adversarial perturbations,
both of which are motivated by minimizing human ability to detect changes
between the perturbed and source image. The first of these, the Edge-Aware
method, reduces the magnitude of perturbations permitted in smooth regions of
an image where changes are more easily detected. Our second method, the
Color-Aware method, performs the perturbation in a color space which accurately
captures human ability to distinguish differences in colors, thus reducing the
perceived change. The Color-Aware and Edge-Aware methods can also be
implemented simultaneously, resulting in image perturbations which account for
both human color perception and sensitivity to changes in homogeneous regions.
Because Edge-Aware and Color-Aware modifications exist for many image
perturbations techniques, we also focus on computation to demonstrate their
potential for use within more complex perturbation schemes. We empirically
demonstrate that the Color-Aware and Edge-Aware perturbations we consider
effectively cause misclassification, are less distinguishable to human
perception, and are as easy to compute as the most efficient image perturbation
techniques. Code and demo available at
https://github.com/rbassett3/Color-and-Edge-Aware-Perturbations | [
"cs.CV",
"eess.IV",
"stat.ML",
"68T45, 62F35"
] |
Instance-level contrastive learning techniques, which rely on data
augmentation and a contrastive loss function, have found great success in the
domain of visual representation learning. They are not suitable for exploiting
the rich dynamical structure of video however, as operations are done on many
augmented instances. In this paper we propose "Video Cross-Stream Prototypical
Contrasting", a novel method which predicts consistent prototype assignments
from both RGB and optical flow views, operating on sets of samples.
Specifically, we alternate the optimization process; while optimizing one of
the streams, all views are mapped to one set of stream prototype vectors. Each
of the assignments is predicted with all views except the one matching the
prediction, pushing representations closer to their assigned prototypes. As a
result, more efficient video embeddings with ingrained motion information are
learned, without the explicit need for optical flow computation during
inference. We obtain state-of-the-art results on nearest neighbour video
retrieval and action recognition, outperforming previous best by +3.2% on
UCF101 using the S3D backbone (90.5% Top-1 acc), and by +7.2% on UCF101 and
+15.1% on HMDB51 using the R(2+1)D backbone. | [
"cs.CV"
] |
A photo captured with bokeh effect often means objects in focus are sharp
while the out-of-focus areas are all blurred. DSLR can easily render this kind
of effect naturally. However, due to the limitation of sensors, smartphones
cannot capture images with depth-of-field effects directly. In this paper, we
propose a novel generator called Glass-Net, which generates bokeh images not
relying on complex hardware. Meanwhile, the GAN-based method and perceptual
loss are combined for rendering a realistic bokeh effect in the stage of
finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in
our network, which ensures our tflite model with IN can be accelerated on
smartphone GPU. Experiments show that our method is able to render a
high-quality bokeh effect and process one $1024 \times 1536$ pixel image in 1.9
seconds on all smartphone chipsets. This approach ranked First in AIM 2020
Rendering Realistic Bokeh Challenge Track 1 \& Track 2. | [
"cs.CV",
"eess.IV"
] |
We address the task of multi-view image-to-image translation for person image
generation. The goal is to synthesize photo-realistic multi-view images with
pose-consistency across all views. Our proposed end-to-end framework is based
on a joint learning of multiple unpaired image-to-image translation models, one
per camera viewpoint. The joint learning is imposed by constraints on the
shared 3D human pose in order to encourage the 2D pose projections in all views
to be consistent. Experimental results on the CMU-Panoptic dataset demonstrate
the effectiveness of the suggested framework in generating photo-realistic
images of persons with new poses that are more consistent across all views in
comparison to a standard Image-to-Image baseline. The code is available at:
https://github.com/sony-si/MultiView-Img2Img | [
"cs.CV"
] |
Real-world applications of machine learning tools in high-stakes domains are
often regulated to be fair, in the sense that the predicted target should
satisfy some quantitative notion of parity with respect to a protected
attribute. However, the exact tradeoff between fairness and accuracy with a
real-valued target is not clear. In this paper, we characterize the inherent
tradeoff between statistical parity and accuracy in the regression setting by
providing a lower bound on the error of any fair regressor. Our lower bound is
sharp, algorithm-independent, and admits a simple interpretation: when the
moments of the target differ between groups, any fair algorithm has to make a
large error on at least one of the groups. We further extend this result to
give a lower bound on the joint error of any (approximately) fair algorithm,
using the Wasserstein distance to measure the quality of the approximation. On
the upside, we establish the first connection between individual fairness,
accuracy parity, and the Wasserstein distance by showing that if a regressor is
individually fair, it also approximately verifies the accuracy parity, where
the gap is given by the Wasserstein distance between the two groups. Inspired
by our theoretical results, we develop a practical algorithm for fair
regression through the lens of representation learning, and conduct experiments
on a real-world dataset to corroborate our findings. | [
"cs.LG",
"cs.AI",
"cs.CY",
"stat.ML"
] |
Hyperbolic embeddings have recently gained attention in machine learning due
to their ability to represent hierarchical data more accurately and succinctly
than their Euclidean analogues. However, multi-relational knowledge graphs
often exhibit multiple simultaneous hierarchies, which current hyperbolic
models do not capture. To address this, we propose a model that embeds
multi-relational graph data in the Poincar\'e ball model of hyperbolic space.
Our Multi-Relational Poincar\'e model (MuRP) learns relation-specific
parameters to transform entity embeddings by M\"obius matrix-vector
multiplication and M\"obius addition. Experiments on the hierarchical WN18RR
knowledge graph show that our Poincar\'e embeddings outperform their Euclidean
counterpart and existing embedding methods on the link prediction task,
particularly at lower dimensionality. | [
"cs.LG",
"stat.ML"
] |
Recent works on single-image super-resolution are concentrated on improving
performance through enhancing spatial encoding between convolutional layers. In
this paper, we focus on modeling the correlations between channels of
convolutional features. We present an effective deep residual network based on
squeeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR)
image from low-resolution (LR) image. SEBlock is used to adaptively recalibrate
channel-wise feature mappings. Further, short connections between each SEBlock
are used to remedy information loss. Extensive experiments show that our model
can achieve the state-of-the-art performance and get finer texture details. | [
"cs.CV"
] |
This paper presents a method to differentiate the foreground objects from the
background of a color image. Firstly a color image of any size is input for
processing. The algorithm converts it to a grayscale image. Next we apply canny
edge detector to find the boundary of the foreground object. We concentrate to
find the maximum distance between each boundary pixel column wise and row wise
and we fill the region that is bound by the edges. Thus we are able to extract
the grayscale values of pixels that are in the bounded region and convert the
grayscale image back to original color image containing only the foreground
object. | [
"cs.CV"
] |
3D point cloud generation by the deep neural network from a single image has
been attracting more and more researchers' attention. However,
recently-proposed methods require the objects be captured with relatively clean
backgrounds, fixed viewpoint, while this highly limits its application in the
real environment. To overcome these drawbacks, we proposed to integrate the
prior 3D shape knowledge into the network to guide the 3D generation. By taking
additional 3D information, the proposed network can handle the 3D object
generation from a single real image captured from any viewpoint and complex
background. Specifically, giving a query image, we retrieve the nearest shape
model from a pre-prepared 3D model database. Then, the image together with the
retrieved shape model is fed into the proposed network to generate the
fine-grained 3D point cloud. The effectiveness of our proposed framework has
been verified on different kinds of datasets. Experimental results show that
the proposed framework achieves state-of-the-art accuracy compared to other
volumetric-based and point set generation methods. Furthermore, the proposed
framework works well for real images in complex backgrounds with various view
angles. | [
"cs.CV"
] |
In this paper, we propose a simple but effective method for fast image
segmentation. We re-examine the locality-preserving character of spectral
clustering by constructing a graph over image regions with both global and
local connections. Our novel approach to build graph connections relies on two
key observations: 1) local region pairs that co-occur frequently will have a
high probability to reside on a common object; 2) spatially distant regions in
a common object often exhibit similar visual saliency, which implies their
neighborship in a manifold. We present a novel energy function to efficiently
conduct graph partitioning. Based on multiple high quality partitions, we show
that the generated eigenvector histogram based representation can automatically
drive effective unary potentials for a hierarchical random field model to
produce multi-class segmentation. Sufficient experiments, on the BSDS500
benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the
competitive segmentation accuracy and significantly improved efficiency of our
proposed method compared with other state of the arts. | [
"cs.CV"
] |
In this paper, a new population-guided parallel learning scheme is proposed
to enhance the performance of off-policy reinforcement learning (RL). In the
proposed scheme, multiple identical learners with their own value-functions and
policies share a common experience replay buffer, and search a good policy in
collaboration with the guidance of the best policy information. The key point
is that the information of the best policy is fused in a soft manner by
constructing an augmented loss function for policy update to enlarge the
overall search region by the multiple learners. The guidance by the previous
best policy and the enlarged range enable faster and better policy search.
Monotone improvement of the expected cumulative return by the proposed scheme
is proved theoretically. Working algorithms are constructed by applying the
proposed scheme to the twin delayed deep deterministic (TD3) policy gradient
algorithm. Numerical results show that the constructed algorithm outperforms
most of the current state-of-the-art RL algorithms, and the gain is significant
in the case of sparse reward environment. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Video Question Answering (Video QA) is a powerful testbed to develop new AI
capabilities. This task necessitates learning to reason about objects,
relations, and events across visual and linguistic domains in space-time.
High-level reasoning demands lifting from associative visual pattern
recognition to symbol-like manipulation over objects, their behavior and
interactions. Toward reaching this goal we propose an object-oriented reasoning
approach in that video is abstracted as a dynamic stream of interacting
objects. At each stage of the video event flow, these objects interact with
each other, and their interactions are reasoned about with respect to the query
and under the overall context of a video. This mechanism is materialized into a
family of general-purpose neural units and their multi-level architecture
called Hierarchical Object-oriented Spatio-Temporal Reasoning (HOSTR) networks.
This neural model maintains the objects' consistent lifelines in the form of a
hierarchically nested spatio-temporal graph. Within this graph, the dynamic
interactive object-oriented representations are built up along the video
sequence, hierarchically abstracted in a bottom-up manner, and converge toward
the key information for the correct answer. The method is evaluated on multiple
major Video QA datasets and establishes new state-of-the-arts in these tasks.
Analysis into the model's behavior indicates that object-oriented reasoning is
a reliable, interpretable and efficient approach to Video QA. | [
"cs.CV"
] |
Evaluating generative adversarial networks (GANs) is inherently challenging.
In this paper, we revisit several representative sample-based evaluation
metrics for GANs, and address the problem of how to evaluate the evaluation
metrics. We start with a few necessary conditions for metrics to produce
meaningful scores, such as distinguishing real from generated samples,
identifying mode dropping and mode collapsing, and detecting overfitting. With
a series of carefully designed experiments, we comprehensively investigate
existing sample-based metrics and identify their strengths and limitations in
practical settings. Based on these results, we observe that kernel Maximum Mean
Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to
satisfy most of the desirable properties, provided that the distances between
samples are computed in a suitable feature space. Our experiments also unveil
interesting properties about the behavior of several popular GAN models, such
as whether they are memorizing training samples, and how far they are from
learning the target distribution. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
In this paper we introduce a new method for text detection in natural images.
The method comprises two contributions: First, a fast and scalable engine to
generate synthetic images of text in clutter. This engine overlays synthetic
text to existing background images in a natural way, accounting for the local
3D scene geometry. Second, we use the synthetic images to train a
Fully-Convolutional Regression Network (FCRN) which efficiently performs text
detection and bounding-box regression at all locations and multiple scales in
an image. We discuss the relation of FCRN to the recently-introduced YOLO
detector, as well as other end-to-end object detection systems based on deep
learning. The resulting detection network significantly out performs current
methods for text detection in natural images, achieving an F-measure of 84.2%
on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per
second on a GPU. | [
"cs.CV"
] |
Ultrasound tongue imaging is widely used for speech production research, and
it has attracted increasing attention as its potential applications seem to be
evident in many different fields, such as the visual biofeedback tool for
second language acquisition and silent speech interface. Unlike previous
studies, here we explore the feasibility of age estimation using the ultrasound
tongue image of the speakers. Motivated by the success of deep learning, this
paper leverages deep learning on this task. We train a deep convolutional
neural network model on the UltraSuite dataset. The deep model achieves mean
absolute error (MAE) of 2.03 for the data from typically developing children,
while MAE is 4.87 for the data from the children with speech sound disorders,
which suggest that age estimation using ultrasound is more challenging for the
children with speech sound disorder. The developed method can be used a tool to
evaluate the performance of speech therapy sessions. It is also worthwhile to
notice that, although we leverage the ultrasound tongue imaging for our study,
the proposed methods may also be extended to other imaging modalities (e.g.
MRI) to assist the studies on speech production. | [
"cs.CV"
] |
In this paper, we have proposed an extended version of Absolute Moment Block
Truncation Coding (AMBTC) to compress images. Generally the elements of a
bitplane used in the variants of Block Truncation Coding (BTC) are of size 1
bit. But it has been extended to two bits in the proposed method. Number of
statistical moments preserved to reconstruct the compressed has also been
raised from 2 to 4. Hence, the quality of the reconstructed images has been
improved significantly from 33.62 to 38.12 with the increase in bpp by 1. The
increased bpp (3) is further reduced to 1.75in multiple levels: in one level,
by dropping 4 elements of the bitplane in such a away that the pixel values of
the dropped elements can easily be interpolated with out much of loss in the
quality, in level two, eight elements are dropped and reconstructed later and
in level three, the size of the statistical moments is reduced. The experiments
were carried over standard images of varying intensities. In all the cases, the
proposed method outperforms the existing AMBTC technique in terms of both PSNR
and bpp. | [
"cs.CV"
] |
In this paper, a color texture image retrieval framework is proposed based on
Shearlet domain modeling using Copula multivariate model. In the proposed
framework, Gaussian Copula is used to model the dependencies between different
sub-bands of the Non Subsample Shearlet Transform (NSST) and non-Gaussian
models are used for marginal modeling of the coefficients. Six different
schemes are proposed for modeling NSST coefficients based on the four types of
neighboring defined; moreover, Kullback Leibler Divergence(KLD) close form is
calculated in different situations for the two Gaussian Copula and non Gaussian
functions in order to investigate the similarities in the proposed retrieval
framework. The Jeffery divergence (JD) criterion, which is a symmetrical
version of KLD, is used for investigating similarities in the proposed
framework. We have implemented our experiments on four texture image retrieval
benchmark datasets, the results of which show the superiority of the proposed
framework over the existing state-of-the-art methods. In addition, the
retrieval time of the proposed framework is also analyzed in the two steps of
feature extraction and similarity matching, which also shows that the proposed
framework enjoys an appropriate retrieval time. | [
"cs.CV",
"eess.IV",
"68T99",
"I.4.9"
] |
The smallest eigenvectors of the graph Laplacian are well-known to provide a
succinct representation of the geometry of a weighted graph. In reinforcement
learning (RL), where the weighted graph may be interpreted as the state
transition process induced by a behavior policy acting on the environment,
approximating the eigenvectors of the Laplacian provides a promising approach
to state representation learning. However, existing methods for performing this
approximation are ill-suited in general RL settings for two main reasons:
First, they are computationally expensive, often requiring operations on large
matrices. Second, these methods lack adequate justification beyond simple,
tabular, finite-state settings. In this paper, we present a fully general and
scalable method for approximating the eigenvectors of the Laplacian in a
model-free RL context. We systematically evaluate our approach and empirically
show that it generalizes beyond the tabular, finite-state setting. Even in
tabular, finite-state settings, its ability to approximate the eigenvectors
outperforms previous proposals. Finally, we show the potential benefits of
using a Laplacian representation learned using our method in goal-achieving RL
tasks, providing evidence that our technique can be used to significantly
improve the performance of an RL agent. | [
"cs.LG",
"stat.ML"
] |
Accurate prediction of crop yield supported by scientific and domain-relevant
insights, can help improve agricultural breeding, provide monitoring across
diverse climatic conditions and thereby protect against climatic challenges to
crop production including erratic rainfall and temperature variations. We used
historical performance records from Uniform Soybean Tests (UST) in North
America spanning 13 years of data to build a Long Short Term Memory - Recurrent
Neural Network based model to dissect and predict genotype response in
multiple-environments by leveraging pedigree relatedness measures along with
weekly weather parameters. Additionally, for providing explainability of the
important time-windows in the growing season, we developed a model based on
temporal attention mechanism. The combination of these two models outperformed
random forest (RF), LASSO regression and the data-driven USDA model for yield
prediction. We deployed this deep learning framework as a 'hypotheses
generation tool' to unravel GxExM relationships. Attention-based time series
models provide a significant advancement in interpretability of yield
prediction models. The insights provided by explainable models are applicable
in understanding how plant breeding programs can adapt their approaches for
global climate change, for example identification of superior varieties for
commercial release, intelligent sampling of testing environments in variety
development, and integrating weather parameters for a targeted breeding
approach. Using DL models as hypothesis generation tools will enable
development of varieties with plasticity response in variable climatic
conditions. We envision broad applicability of this approach (via conducting
sensitivity analysis and "what-if" scenarios) for soybean and other crop
species under different climatic conditions. | [
"cs.LG",
"stat.ML"
] |
This study assesses the efficiency of several popular machine learning
approaches in the prediction of molecular binding affinity: CatBoost, Graph
Attention Neural Network, and Bidirectional Encoder Representations from
Transformers. The models were trained to predict binding affinities in terms of
inhibition constants $K_i$ for pairs of proteins and small organic molecules.
First two approaches use thoroughly selected physico-chemical features, while
the third one is based on textual molecular representations - it is one of the
first attempts to apply Transformer-based predictors for the binding affinity.
We also discuss the visualization of attention layers within the Transformer
approach in order to highlight the molecular sites responsible for
interactions. All approaches are free from atomic spatial coordinates thus
avoiding bias from known structures and being able to generalize for compounds
with unknown conformations. The achieved accuracy for all suggested approaches
prove their potential in high throughput screening. | [
"cs.LG",
"physics.chem-ph"
] |
One important challenge of applying deep learning to electronic health
records (EHR) is the complexity of their multimodal structure. EHR usually
contains a mixture of structured (codes) and unstructured (free-text) data with
sparse and irregular longitudinal features -- all of which doctors utilize when
making decisions. In the deep learning regime, determining how different
modality representations should be fused together is a difficult problem, which
is often addressed by handcrafted modeling and intuition. In this work, we
extend state-of-the-art neural architecture search (NAS) methods and propose
MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across
multimodal fusion strategies and modality-specific architectures for the first
time. We demonstrate empirically that our MUFASA method outperforms established
unimodal NAS on public EHR data with comparable computation costs. In addition,
MUFASA produces architectures that outperform Transformer and Evolved
Transformer. Compared with these baselines on CCS diagnosis code prediction,
our discovered models improve top-5 recall from 0.88 to 0.91 and demonstrate
the ability to generalize to other EHR tasks. Studying our top architecture in
depth, we provide empirical evidence that MUFASA's improvements are derived
from its ability to both customize modeling for each data modality and find
effective fusion strategies. | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
The dominant paradigm in spatiotemporal action detection is to classify
actions using spatiotemporal features learned by 2D or 3D Convolutional
Networks. We argue that several actions are characterized by their context,
such as relevant objects and actors present in the video. To this end, we
introduce an architecture based on self-attention and Graph Convolutional
Networks in order to model contextual cues, such as actor-actor and
actor-object interactions, to improve human action detection in video. We are
interested in achieving this in a weakly-supervised setting, i.e. using as less
annotations as possible in terms of action bounding boxes. Our model aids
explainability by visualizing the learned context as an attention map, even for
actions and objects unseen during training. We evaluate how well our model
highlights the relevant context by introducing a quantitative metric based on
recall of objects retrieved by attention maps. Our model relies on a 3D
convolutional RGB stream, and does not require expensive optical flow
computation. We evaluate our models on the DALY dataset, which consists of
human-object interaction actions. Experimental results show that our
contextualized approach outperforms a baseline action detection approach by
more than 2 points in Video-mAP. Code is available at
\url{https://github.com/micts/acgcn} | [
"cs.LG",
"cs.CV"
] |
The determination of accurate bathymetric information is a key element for
near offshore activities, hydrological studies such as coastal engineering
applications, sedimentary processes, hydrographic surveying as well as
archaeological mapping and biological research. UAV imagery processed with
Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide
a low-cost alternative to established shallow seabed mapping techniques
offering as well the important visual information. Nevertheless, water
refraction poses significant challenges on depth determination. Till now, this
problem has been addressed through customized image-based refraction correction
algorithms or by modifying the collinearity equation. In this paper, in order
to overcome the water refraction errors, we employ machine learning tools that
are able to learn the systematic underestimation of the estimated depths. In
the proposed approach, based on known depth observations from bathymetric LiDAR
surveys, an SVR model was developed able to estimate more accurately the real
depths of point clouds derived from SfM-MVS procedures. Experimental results
over two test sites along with the performed quantitative validation indicated
the high potential of the developed approach. | [
"cs.CV",
"cs.LG"
] |
Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov
equivalence class are fundamental tasks in graphical causal analysis. In this
paper, we show that these tasks can be performed in polynomial time, solving a
long-standing open problem in this area. Our algorithms are effective and
easily implementable. Experimental results show that the algorithms
significantly outperform state-of-the-art methods. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Intelligent agents must pursue their goals in complex environments with
partial information and often limited computational capacity. Reinforcement
learning methods have achieved great success by creating agents that optimize
engineered reward functions, but which often struggle to learn in sparse-reward
environments, generally require many environmental interactions to perform
well, and are typically computationally very expensive. Active inference is a
model-based approach that directs agents to explore uncertain states while
adhering to a prior model of their goal behaviour. This paper introduces an
active inference agent which minimizes the novel free energy of the expected
future. Our model is capable of solving sparse-reward problems with a very high
sample efficiency due to its objective function, which encourages directed
exploration of uncertain states. Moreover, our model is computationally very
light and can operate in a fully online manner while achieving comparable
performance to offline RL methods. We showcase the capabilities of our model by
solving the mountain car problem, where we demonstrate its superior exploration
properties and its robustness to observation noise, which in fact improves
performance. We also introduce a novel method for approximating the prior model
from the reward function, which simplifies the expression of complex objectives
and improves performance over previous active inference approaches. | [
"cs.LG",
"cs.AI",
"stat.ML",
"I.2"
] |
We introduce a data-free quantization method for deep neural networks that
does not require fine-tuning or hyperparameter selection. It achieves
near-original model performance on common computer vision architectures and
tasks. 8-bit fixed-point quantization is essential for efficient inference on
modern deep learning hardware. However, quantizing models to run in 8-bit is a
non-trivial task, frequently leading to either significant performance
reduction or engineering time spent on training a network to be amenable to
quantization. Our approach relies on equalizing the weight ranges in the
network by making use of a scale-equivariance property of activation functions.
In addition the method corrects biases in the error that are introduced during
quantization. This improves quantization accuracy performance, and can be
applied to many common computer vision architectures with a straight forward
API call. For common architectures, such as the MobileNet family, we achieve
state-of-the-art quantized model performance. We further show that the method
also extends to other computer vision architectures and tasks such as semantic
segmentation and object detection. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Recent research has explored the possibility of automatically deducing
information such as gender, age and race of an individual from their biometric
data. While the face modality has been extensively studied in this regard, the
iris modality less so. In this paper, we first review the medical literature to
establish a biological basis for extracting gender and race cues from the iris.
Then, we demonstrate that it is possible to use simple texture descriptors,
like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary
Patterns), to extract gender and race attributes from an NIR ocular image used
in a typical iris recognition system. The proposed method predicts gender and
race from a single eye image with an accuracy of 86% and 90%, respectively. In
addition, the following analysis are conducted: (a) the role of different parts
of the ocular region on attribute prediction; (b) the influence of gender on
race prediction, and vice-versa; (c) the impact of eye color on gender and race
prediction; (d) the impact of image blur on gender and race prediction; (e) the
generalizability of the method across different datasets; and (f) the
consistency of prediction performance across the left and right eyes. | [
"cs.CV"
] |
Automated medical report generation in spine radiology, i.e., given spinal
medical images and directly create radiologist-level diagnosis reports to
support clinical decision making, is a novel yet fundamental study in the
domain of artificial intelligence in healthcare. However, it is incredibly
challenging because it is an extremely complicated task that involves visual
perception and high-level reasoning processes. In this paper, we propose the
neural-symbolic learning (NSL) framework that performs human-like learning by
unifying deep neural learning and symbolic logical reasoning for the spinal
medical report generation. Generally speaking, the NSL framework firstly
employs deep neural learning to imitate human visual perception for detecting
abnormalities of target spinal structures. Concretely, we design an adversarial
graph network that interpolates a symbolic graph reasoning module into a
generative adversarial network through embedding prior domain knowledge,
achieving semantic segmentation of spinal structures with high complexity and
variability. NSL secondly conducts human-like symbolic logical reasoning that
realizes unsupervised causal effect analysis of detected entities of
abnormalities through meta-interpretive learning. NSL finally fills these
discoveries of target diseases into a unified template, successfully achieving
a comprehensive medical report generation. When it employed in a real-world
clinical dataset, a series of empirical studies demonstrate its capacity on
spinal medical report generation as well as show that our algorithm remarkably
exceeds existing methods in the detection of spinal structures. These indicate
its potential as a clinical tool that contributes to computer-aided diagnosis. | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] |
We propose a novel unsupervised approach based on a two-stage object-centric
adversarial framework that only needs object regions for detecting frame-level
local anomalies in videos. The first stage consists in learning the
correspondence between the current appearance and past gradient images of
objects in scenes deemed normal, allowing us to either generate the past
gradient from current appearance or the reverse. The second stage extracts the
partial reconstruction errors between real and generated images (appearance and
past gradient) with normal object behaviour, and trains a discriminator in an
adversarial fashion. In inference mode, we employ the trained image generators
with the adversarially learned binary classifier for outputting region-level
anomaly detection scores. We tested our method on four public benchmarks, UMN,
UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial
approach yields competitive or even superior results compared to
state-of-the-art methods. | [
"cs.CV",
"cs.LG"
] |
Statistical machine learning methods are increasingly used for neuroimaging
data analysis. Their main virtue is their ability to model high-dimensional
datasets, e.g. multivariate analysis of activation images or resting-state time
series. Supervised learning is typically used in decoding or encoding settings
to relate brain images to behavioral or clinical observations, while
unsupervised learning can uncover hidden structures in sets of images (e.g.
resting state functional MRI) or find sub-populations in large cohorts. By
considering different functional neuroimaging applications, we illustrate how
scikit-learn, a Python machine learning library, can be used to perform some
key analysis steps. Scikit-learn contains a very large set of statistical
learning algorithms, both supervised and unsupervised, and its application to
neuroimaging data provides a versatile tool to study the brain. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Multi-object tracking is a fundamental vision problem that has been studied
for a long time. As deep learning brings excellent performances to object
detection algorithms, Tracking by Detection (TBD) has become the mainstream
tracking framework. Despite the success of TBD, this two-step method is too
complicated to train in an end-to-end manner and induces many challenges as
well, such as insufficient exploration of video spatial-temporal information,
vulnerability when facing object occlusion, and excessive reliance on detection
results. To address these challenges, we propose a concise end-to-end model
TubeTK which only needs one step training by introducing the ``bounding-tube"
to indicate temporal-spatial locations of objects in a short video clip. TubeTK
provides a novel direction of multi-object tracking, and we demonstrate its
potential to solve the above challenges without bells and whistles. We analyze
the performance of TubeTK on several MOT benchmarks and provide empirical
evidence to show that TubeTK has the ability to overcome occlusions to some
extent without any ancillary technologies like Re-ID. Compared with other
methods that adopt private detection results, our one-stage end-to-end model
achieves state-of-the-art performances even if it adopts no ready-made
detection results. We hope that the proposed TubeTK model can serve as a simple
but strong alternative for video-based MOT task. The code and models are
available at https://github.com/BoPang1996/TubeTK. | [
"cs.CV"
] |
Drug-induced parkinsonism affects many older adults with dementia, often
causing gait disturbances. New advances in vision-based human pose-estimation
have opened possibilities for frequent and unobtrusive analysis of gait in
residential settings. This work proposes novel spatial-temporal graph
convolutional network (ST-GCN) architectures and training procedures to predict
clinical scores of parkinsonism in gait from video of individuals with
dementia. We propose a two-stage training approach consisting of a
self-supervised pretraining stage that encourages the ST-GCN model to learn
about gait patterns before predicting clinical scores in the finetuning stage.
The proposed ST-GCN models are evaluated on joint trajectories extracted from
video and are compared against traditional (ordinal, linear, random forest)
regression models and temporal convolutional network baselines. Three 2D human
pose-estimation libraries (OpenPose, Detectron, AlphaPose) and the Microsoft
Kinect (2D and 3D) are used to extract joint trajectories of 4787 natural
walking bouts from 53 older adults with dementia. A subset of 399 walks from 14
participants is annotated with scores of parkinsonism severity on the gait
criteria of the Unified Parkinson's Disease Rating Scale (UPDRS) and the
Simpson-Angus Scale (SAS). Our results demonstrate that ST-GCN models operating
on 3D joint trajectories extracted from the Kinect consistently outperform all
other models and feature sets. Prediction of parkinsonism scores in natural
walking bouts of unseen participants remains a challenging task, with the best
models achieving macro-averaged F1-scores of 0.53 +/- 0.03 and 0.40 +/- 0.02
for UPDRS-gait and SAS-gait, respectively. Pre-trained model and demo code for
this work is available:
https://github.com/TaatiTeam/stgcn_parkinsonism_prediction. | [
"cs.CV",
"cs.LG"
] |
Person re-identification (re-id) remains challenging due to significant
intra-class variations across different cameras. Recently, there has been a
growing interest in using generative models to augment training data and
enhance the invariance to input changes. The generative pipelines in existing
methods, however, stay relatively separate from the discriminative re-id
learning stages. Accordingly, re-id models are often trained in a
straightforward manner on the generated data. In this paper, we seek to improve
learned re-id embeddings by better leveraging the generated data. To this end,
we propose a joint learning framework that couples re-id learning and data
generation end-to-end. Our model involves a generative module that separately
encodes each person into an appearance code and a structure code, and a
discriminative module that shares the appearance encoder with the generative
module. By switching the appearance or structure codes, the generative module
is able to generate high-quality cross-id composed images, which are online fed
back to the appearance encoder and used to improve the discriminative module.
The proposed joint learning framework renders significant improvement over the
baseline without using generated data, leading to the state-of-the-art
performance on several benchmark datasets. | [
"cs.CV"
] |
Understanding the predictions made by Artificial Intelligence (AI) systems is
becoming more and more important as deep learning models are used for
increasingly complex and high-stakes tasks. Saliency mapping - an easily
interpretable visual attribution method - is one important tool for this, but
existing formulations are limited by either computational cost or architectural
constraints. We therefore propose Hierarchical Perturbation, a very fast and
completely model-agnostic method for explaining model predictions with robust
saliency maps. Using standard benchmarks and datasets, we show that our
saliency maps are of competitive or superior quality to those generated by
existing model-agnostic methods - and are over 20X faster to compute. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
The research on mortality is an active area of research for any country where
the conclusions are driven from the provided data and conditions. The domain
knowledge is an essential but not a mandatory skill (though some knowledge is
still required) in order to derive conclusions based on data intuition using
machine learning and data science practices. The purpose of conducting this
project was to derive conclusions based on the statistics from the provided
dataset and predict label(s) of the dataset using supervised or unsupervised
learning algorithms. The study concluded (based on a sample) life expectancy
regardless of gender, and their central tendencies; Marital status of the
people also affected how frequent deaths were for each of them. The study also
helped in finding out that due to more categorical and numerical data, anomaly
detection or under-sampling could be a viable solution since there are
possibilities of more class labels than the other(s). The study shows that
machine learning predictions aren't as viable for the data as it might be
apparent. | [
"cs.LG",
"cs.CY"
] |
In adversarial environments, one side could gain an advantage by identifying
the opponent's strategy. For example, in combat games, if an opponents strategy
is identified as overly aggressive, one could lay a trap that exploits the
opponent's aggressive nature. However, an opponent's strategy is not always
apparent and may need to be estimated from observations of their actions. This
paper proposes to use inverse reinforcement learning (IRL) to identify
strategies in adversarial environments. Specifically, the contributions of this
work are 1) the demonstration of this concept on gaming combat data generated
from three pre-defined strategies and 2) the framework for using IRL to achieve
strategy identification. The numerical experiments demonstrate that the
recovered rewards can be identified using a variety of techniques. In this
paper, the recovered reward are visually displayed, clustered using
unsupervised learning, and classified using a supervised learner. | [
"cs.LG",
"cs.AI",
"cs.GT"
] |
Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for
continuous action settings that is not applicable to discrete action settings.
Many important settings involve discrete actions, however, and so here we
derive an alternative version of the Soft Actor-Critic algorithm that is
applicable to discrete action settings. We then show that, even without any
hyperparameter tuning, it is competitive with the tuned model-free
state-of-the-art on a selection of games from the Atari suite. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Matching local features across images is a fundamental problem in computer
vision. Targeting towards high accuracy and efficiency, we propose Seeded Graph
Matching Network, a graph neural network with sparse structure to reduce
redundant connectivity and learn compact representation. The network consists
of 1) Seeding Module, which initializes the matching by generating a small set
of reliable matches as seeds. 2) Seeded Graph Neural Network, which utilizes
seed matches to pass messages within/across images and predicts assignment
costs. Three novel operations are proposed as basic elements for message
passing: 1) Attentional Pooling, which aggregates keypoint features within the
image to seed matches. 2) Seed Filtering, which enhances seed features and
exchanges messages across images. 3) Attentional Unpooling, which propagates
seed features back to original keypoints. Experiments show that our method
reduces computational and memory complexity significantly compared with typical
attention-based networks while competitive or higher performance is achieved. | [
"cs.CV"
] |
Attention models have had a significant positive impact on deep learning
across a range of tasks. However previous attempts at integrating attention
with reinforcement learning have failed to produce significant improvements. We
propose the first combination of self attention and reinforcement learning that
is capable of producing significant improvements, including new state of the
art results in the Arcade Learning Environment. Unlike the selective attention
models used in previous attempts, which constrain the attention via
preconceived notions of importance, our implementation utilises the Markovian
properties inherent in the state input. Our method produces a faithful
visualisation of the policy, focusing on the behaviour of the agent. Our
experiments demonstrate that the trained policies use multiple simultaneous
foci of attention, and are able to modulate attention over time to deal with
situations of partial observability. | [
"cs.LG",
"stat.ML"
] |
Hierarchical reinforcement learning has demonstrated significant success at
solving difficult reinforcement learning (RL) tasks. Previous works have
motivated the use of hierarchy by appealing to a number of intuitive benefits,
including learning over temporally extended transitions, exploring over
temporally extended periods, and training and exploring in a more semantically
meaningful action space, among others. However, in fully observed, Markovian
settings, it is not immediately clear why hierarchical RL should provide
benefits over standard "shallow" RL architectures. In this work, we isolate and
evaluate the claimed benefits of hierarchical RL on a suite of tasks
encompassing locomotion, navigation, and manipulation. Surprisingly, we find
that most of the observed benefits of hierarchy can be attributed to improved
exploration, as opposed to easier policy learning or imposed hierarchical
structures. Given this insight, we present exploration techniques inspired by
hierarchy that achieve performance competitive with hierarchical RL while at
the same time being much simpler to use and implement. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Light field imaging is characterized by capturing brightness, color, and
directional information of light rays in a scene. This leads to image
representations with huge amount of data that require efficient coding schemes.
In this paper, lenslet images are rendered into sub-aperture images. These
images are organized as a pseudo-sequence input for the HEVC video codec. To
better exploit redundancy among the neighboring sub-aperture images and
consequently decrease the distances between a sub-aperture image and its
references used for prediction, sub-aperture images are divided into four
smaller groups that are scanned in a serpentine order. The most central
sub-aperture image, which has the highest similarity to all the other images,
is used as the initial reference image for each of the four regions.
Furthermore, a structure is defined that selects spatially adjacent
sub-aperture images as prediction references with the highest similarity to the
current image. In this way, encoding efficiency increases, and furthermore it
leads to a higher similarity among the co-located Coding Three Units (CTUs).
The similarities among the co-located CTUs are exploited to predict Coding Unit
depths.Moreover, independent encoding of each group division enables parallel
processing, that along with the proposed coding unit depth prediction decrease
the encoding execution time by almost 80% on average. Simulation results show
that Rate-Distortion performance of the proposed method has higher compression
gain than the other state-of-the-art lenslet compression methods with lower
computational complexity. | [
"cs.CV"
] |
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