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4,400 | Hard Mixtures of Experts for Large Scale Weakly Supervised Vision | cs.CV | Training convolutional networks (CNN's) that fit on a single GPU with
minibatch stochastic gradient descent has become effective in practice.
However, there is still no effective method for training large CNN's that do
not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
this work we show tha... | computer science |
4,401 | Compressive Sensing Approaches for Autonomous Object Detection in Video
Sequences | cs.CV | Video analytics requires operating with large amounts of data. Compressive
sensing allows to reduce the number of measurements required to represent the
video using the prior knowledge of sparsity of the original signal, but it
imposes certain conditions on the design matrix. The Bayesian compressive
sensing approach r... | computer science |
4,402 | Unsupervised learning of object landmarks by factorized spatial
embeddings | cs.CV | Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations... | computer science |
4,403 | A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image
Denoising | cs.CV | Image denoising techniques are essential to reducing noise levels and
enhancing diagnosis reliability in low-dose computed tomography (CT). Machine
learning based denoising methods have shown great potential in removing the
complex and spatial-variant noises in CT images. However, some residue
artifacts would appear in... | computer science |
4,404 | CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins
from MRI Using Multi-View CNN | cs.CV | Anatomical and biophysical modeling of left atrium (LA) and proximal
pulmonary veins (PPVs) is important for clinical management of several cardiac
diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
and PPVs through visualization. However, there is a strong need for an advanced
image segment... | computer science |
4,405 | Learning Texture Manifolds with the Periodic Spatial GAN | cs.CV | This paper introduces a novel approach to texture synthesis based on
generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the
structure of the input noise distribution by constructing tensors with
different types of dimensions. We call this technique Periodic Spatial GAN
(PSGAN). The PSGAN has sev... | computer science |
4,406 | Learning multiple visual domains with residual adapters | cs.CV | There is a growing interest in learning data representations that work well
for many different types of problems and data. In this paper, we look in
particular at the task of learning a single visual representation that can be
successfully utilized in the analysis of very different types of images, from
dog breeds to s... | computer science |
4,407 | Real-Time Background Subtraction Using Adaptive Sampling and Cascade of
Gaussians | stat.ML | Background-Foreground classification is a fundamental well-studied problem in
computer vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired b... | computer science |
4,408 | Decorrelation of Neutral Vector Variables: Theory and Applications | cs.CV | In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian d... | computer science |
4,409 | Dynamic Steerable Blocks in Deep Residual Networks | cs.CV | Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and De... | computer science |
4,410 | Facial Emotion Detection Using Convolutional Neural Networks and
Representational Autoencoder Units | cs.CV | Emotion being a subjective thing, leveraging knowledge and science behind
labeled data and extracting the components that constitute it, has been a
challenging problem in the industry for many years. With the evolution of deep
learning in computer vision, emotion recognition has become a widely-tackled
research problem... | computer science |
4,411 | Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural
Network | stat.ML | Advanced driver assistance systems (ADAS) can be significantly improved with
effective driver action prediction (DAP). Predicting driver actions early and
accurately can help mitigate the effects of potentially unsafe driving
behaviors and avoid possible accidents. In this paper, we formulate driver
action prediction a... | computer science |
4,412 | Sliced Wasserstein Generative Models | cs.CV | In the paper, we introduce a model of sliced optimal transport (SOT), which
measures the distribution affinity with sliced Wasserstein distance (SWD).
Since SWD enjoys the property of factorizing high-dimensional joint
distributions into their multiple one-dimensional marginal distributions, its
dual and primal forms c... | computer science |
4,413 | Unsupervised learning of object frames by dense equivariant image
labelling | cs.CV | One of the key challenges of visual perception is to extract abstract models
of 3D objects and object categories from visual measurements, which are
affected by complex nuisance factors such as viewpoint, occlusion, motion, and
deformations. Starting from the recent idea of viewpoint factorization, we
propose a new app... | computer science |
4,414 | $ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and
Function Parameters | cs.CV | Background: Cardiac MRI derived biventricular mass and function parameters,
such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection
fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically
well established. Image segmentation can be challenging and time-consuming, due
to the complex a... | computer science |
4,415 | Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional
Neural Network | cs.CV | In this paper, we propose a method using a three dimensional convolutional
neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral
(HS) images to obtain a high resolution hyperspectral image. Dimensionality
reduction of the hyperspectral image is performed prior to fusion in order to
significantl... | computer science |
4,416 | A Bayesian algorithm for detecting identity matches and fraud in image
databases | cs.CV | A statistical algorithm for categorizing different types of matches and fraud
in image databases is presented. The approach is based on a generative model of
a graph representing images and connections between pairs of identities,
trained using properties of a matching algorithm between images. | computer science |
4,417 | Deep Learning Based Large-Scale Automatic Satellite Crosswalk
Classification | cs.CV | High-resolution satellite imagery have been increasingly used on remote
sensing classification problems. One of the main factors is the availability of
this kind of data. Even though, very little effort has been placed on the zebra
crossing classification problem. In this letter, crowdsourcing systems are
exploited in ... | computer science |
4,418 | Appearance invariance in convolutional networks with neighborhood
similarity | cs.CV | We present a neighborhood similarity layer (NSL) which induces appearance
invariance in a network when used in conjunction with convolutional layers. We
are motivated by the observation that, even though convolutional networks have
low generalization error, their generalization capability does not extend to
samples whi... | computer science |
4,419 | Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and
Challenges | stat.ML | Semantic segmentation was seen as a challenging computer vision problem few
years ago. Due to recent advancements in deep learning, relatively accurate
solutions are now possible for its use in automated driving. In this paper, the
semantic segmentation problem is explored from the perspective of automated
driving. Mos... | computer science |
4,420 | A step towards procedural terrain generation with GANs | stat.ML | Procedural terrain generation for video games has been traditionally been
done with smartly designed but handcrafted algorithms that generate heightmaps.
We propose a first step toward the learning and synthesis of these using recent
advances in deep generative modelling with openly available satellite imagery
from NAS... | computer science |
4,421 | Generalized Convolutional Neural Networks for Point Cloud Data | cs.CV | The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices
has given the computer vision community 3D information that conventional RGB
cameras cannot provide. This data is often stored as a point cloud. In this
paper, we present a novel method to apply the concept of convolutional neural
networks to th... | computer science |
4,422 | Multi-kernel learning of deep convolutional features for action
recognition | cs.CV | Image understanding using deep convolutional network has reached human-level
performance, yet a closely related problem of video understanding especially,
action recognition has not reached the requisite level of maturity. We combine
multi-kernels based support-vector-machines (SVM) with a multi-stream deep
convolution... | computer science |
4,423 | Multi-Planar Deep Segmentation Networks for Cardiac Substructures from
MRI and CT | stat.ML | Non-invasive detection of cardiovascular disorders from radiology scans
requires quantitative image analysis of the heart and its substructures. There
are well-established measurements that radiologists use for diseases assessment
such as ejection fraction, volume of four chambers, and myocardium mass. These
measuremen... | computer science |
4,424 | Identifying 3 moss species by deep learning, using the "chopped picture"
method | stat.ML | In general, object identification tends not to work well on ambiguous,
amorphous objects such as vegetation. In this study, we developed a simple but
effective approach to identify ambiguous objects and applied the method to
several moss species. As a result, the model correctly classified test images
with accuracy mor... | computer science |
4,425 | VIGAN: Missing View Imputation with Generative Adversarial Networks | cs.CV | In an era when big data are becoming the norm, there is less concern with the
quantity but more with the quality and completeness of the data. In many
disciplines, data are collected from heterogeneous sources, resulting in
multi-view or multi-modal datasets. The missing data problem has been
challenging to address in ... | computer science |
4,426 | Reversible Architectures for Arbitrarily Deep Residual Neural Networks | cs.CV | Recently, deep residual networks have been successfully applied in many
computer vision and natural language processing tasks, pushing the
state-of-the-art performance with deeper and wider architectures. In this work,
we interpret deep residual networks as ordinary differential equations (ODEs),
which have long been s... | computer science |
4,427 | Coupled Ensembles of Neural Networks | cs.CV | We investigate in this paper the architecture of deep convolutional networks.
Building on existing state of the art models, we propose a reconfiguration of
the model parameters into several parallel branches at the global network
level, with each branch being a standalone CNN. We show that this arrangement
is an effici... | computer science |
4,428 | Efficient Column Generation for Cell Detection and Segmentation | cs.CV | We study the problem of instance segmentation in biological images with
crowded and compact cells. We formulate this task as an integer program where
variables correspond to cells and constraints enforce that cells do not
overlap. To solve this integer program, we propose a column generation
formulation where the prici... | computer science |
4,429 | MR Acquisition-Invariant Representation Learning | cs.CV | Voxelwise classification is a popular and effective method for tissue
quantification in brain magnetic resonance imaging (MRI) scans. However, there
are often large differences over sets of MRI scans due to how they were
acquired (i.e. field strength, vendor, protocol), that lead to variation in,
among others, pixel in... | computer science |
4,430 | Learning Multi-grid Generative ConvNets by Minimal Contrastive
Divergence | stat.ML | This paper proposes a minimal contrastive divergence method for learning
energy-based generative ConvNet models of images at multiple grids (or scales)
simultaneously. For each grid, we learn an energy-based probabilistic model
where the energy function is defined by a bottom-up convolutional neural
network (ConvNet or... | computer science |
4,431 | Augmented Robust PCA For Foreground-Background Separation on Noisy,
Moving Camera Video | stat.ML | This work presents a novel approach for robust PCA with total variation
regularization for foreground-background separation and denoising on noisy,
moving camera video. Our proposed algorithm registers the raw (possibly
corrupted) frames of a video and then jointly processes the registered frames
to produce a decomposi... | computer science |
4,432 | On the Capacity of Face Representation | cs.CV | Face recognition is a widely used technology with numerous large-scale
applications, such as surveillance, social media and law enforcement. There has
been tremendous progress in face recognition accuracy over the past few
decades, much of which can be attributed to deep learning-based approaches
during the last five y... | computer science |
4,433 | Robust Photometric Stereo Using Learned Image and Gradient Dictionaries | cs.CV | Photometric stereo is a method for estimating the normal vectors of an object
from images of the object under varying lighting conditions. Motivated by
several recent works that extend photometric stereo to more general objects and
lighting conditions, we study a new robust approach to photometric stereo that
utilizes ... | computer science |
4,434 | Robust Surface Reconstruction from Gradients via Adaptive Dictionary
Regularization | cs.CV | This paper introduces a novel approach to robust surface reconstruction from
photometric stereo normal vector maps that is particularly well-suited for
reconstructing surfaces from noisy gradients. Specifically, we propose an
adaptive dictionary learning based approach that attempts to simultaneously
integrate the grad... | computer science |
4,435 | Adaptive Smoothing in fMRI Data Processing Neural Networks | cs.CV | Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data
processing pipelines to accurately determine brain activity; among them, the
crucial step of spatial smoothing. These pipelines are commonly suboptimal,
given the local optimisation strategy they use, treating each step in
isolation. With the advent... | computer science |
4,436 | Deep Convolutional Neural Networks for Interpretable Analysis of EEG
Sleep Stage Scoring | cs.CV | Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
auto... | computer science |
4,437 | RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE
detection in CT Scans | cs.CV | We describe a deep learning approach for automated brain hemorrhage detection
from computed tomography (CT) scans. Our model emulates the procedure followed
by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
the model sifts through 2D cross-sectional slices while paying close attention
to p... | computer science |
4,438 | Robust Photometric Stereo via Dictionary Learning | cs.CV | Photometric stereo is a method that seeks to reconstruct the normal vectors
of an object from a set of images of the object illuminated under different
light sources. While effective in some situations, classical photometric stereo
relies on a diffuse surface model that cannot handle objects with complex
reflectance pa... | computer science |
4,439 | Anatomical labeling of brain CT scan anomalies using multi-context
nearest neighbor relation networks | cs.CV | This work is an endeavor to develop a deep learning methodology for automated
anatomical labeling of a given region of interest (ROI) in brain computed
tomography (CT) scans. We combine both local and global context to obtain a
representation of the ROI. We then use Relation Networks (RNs) to predict the
corresponding ... | computer science |
4,440 | Multi-level Residual Networks from Dynamical Systems View | stat.ML | Deep residual networks (ResNets) and their variants are widely used in many
computer vision applications and natural language processing tasks. However,
the theoretical principles for designing and training ResNets are still not
fully understood. Recently, several points of view have emerged to try to
interpret ResNet ... | computer science |
4,441 | Randomized Nonnegative Matrix Factorization | stat.ML | Nonnegative matrix factorization (NMF) is a powerful tool for data mining.
However, the emergence of `big data' has severely challenged our ability to
compute this fundamental decomposition using deterministic algorithms. This
paper presents a randomized hierarchical alternating least squares (HALS)
algorithm to comput... | computer science |
4,442 | Deep Matching Autoencoders | cs.CV | Increasingly many real world tasks involve data in multiple modalities or
views. This has motivated the development of many effective algorithms for
learning a common latent space to relate multiple domains. However, most
existing cross-view learning algorithms assume access to paired data for
training. Their applicabi... | computer science |
4,443 | Thoracic Disease Identification and Localization with Limited
Supervision | cs.CV | Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In ... | computer science |
4,444 | Detecting hip fractures with radiologist-level performance using deep
neural networks | cs.CV | We developed an automated deep learning system to detect hip fractures from
frontal pelvic x-rays, an important and common radiological task. Our system
was trained on a decade of clinical x-rays (~53,000 studies) and can be applied
to clinical data, automatically excluding inappropriate and technically
unsatisfactory ... | computer science |
4,445 | ForestHash: Semantic Hashing With Shallow Random Forests and Tiny
Convolutional Networks | cs.CV | Hash codes are efficient data representations for coping with the ever
growing amounts of data. In this paper, we introduce a random forest semantic
hashing scheme that embeds tiny convolutional neural networks (CNN) into
shallow random forests, with near-optimal information-theoretic code
aggregation among trees. We s... | computer science |
4,446 | Deep Video Generation, Prediction and Completion of Human Action
Sequences | cs.CV | Current deep learning results on video generation are limited while there are
only a few first results on video prediction and no relevant significant
results on video completion. This is due to the severe ill-posedness inherent
in these three problems. In this paper, we focus on human action videos, and
propose a gene... | computer science |
4,447 | Prediction of the progression of subcortical brain structures in
Alzheimer's disease from baseline | cs.CV | We propose a method to predict the subject-specific longitudinal progression
of brain structures extracted from baseline MRI, and evaluate its performance
on Alzheimer's disease data. The disease progression is modeled as a trajectory
on a group of diffeomorphisms in the context of large deformation diffeomorphic
metri... | computer science |
4,448 | Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram
Completion | cs.CV | Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180$^\circ$ view of the object. This is impractical in a limited angle
scenario, when the viewi... | computer science |
4,449 | Arbitrary Facial Attribute Editing: Only Change What You Want | cs.CV | Facial attribute editing aims to modify either single or multiple attributes
on a face image. Since it is practically infeasible to collect images with
arbitrarily specified attributes for each person, the generative adversarial
net (GAN) and the encoder-decoder architecture are usually incorporated to
handle this task... | computer science |
4,450 | Deep Image Prior | cs.CV | Deep convolutional networks have become a popular tool for image generation
and restoration. Generally, their excellent performance is imputed to their
ability to learn realistic image priors from a large number of example images.
In this paper, we show that, on the contrary, the structure of a generator
network is suf... | computer science |
4,451 | Label Efficient Learning of Transferable Representations across Domains
and Tasks | stat.ML | We propose a framework that learns a representation transferable across
different domains and tasks in a label efficient manner. Our approach battles
domain shift with a domain adversarial loss, and generalizes the embedding to
novel task using a metric learning-based approach. Our model is simultaneously
optimized on ... | computer science |
4,452 | GANosaic: Mosaic Creation with Generative Texture Manifolds | cs.CV | This paper presents a novel framework for generating texture mosaics with
convolutional neural networks. Our method is called GANosaic and performs
optimization in the latent noise space of a generative texture model, which
allows the transformation of a content image into a mosaic exhibiting the
visual properties of t... | computer science |
4,453 | Folded Recurrent Neural Networks for Future Video Prediction | cs.CV | Future video prediction is an ill-posed Computer Vision problem that recently
received much attention. Its main challenges are the high variability in video
content, the propagation of errors through time, and the non-specificity of the
future frames: given a sequence of past frames there is a continuous
distribution o... | computer science |
4,454 | Hierarchical Bayesian image analysis: from low-level modeling to robust
supervised learning | cs.CV | Within a supervised classification framework, labeled data are used to learn
classifier parameters. Prior to that, it is generally required to perform
dimensionality reduction via feature extraction. These preprocessing steps have
motivated numerous research works aiming at recovering latent variables in an
unsupervise... | computer science |
4,455 | Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural
Networks with Application to Pulmonary Nodule Detection | cs.CV | Motivated by the problem of computer-aided detection (CAD) of pulmonary
nodules, we introduce methods to propagate and fuse uncertainty information in
a multi-stage Bayesian convolutional neural network (CNN) architecture. The
question we seek to answer is "can we take advantage of the model uncertainty
provided by one... | computer science |
4,456 | Learning to detect chest radiographs containing lung nodules using
visual attention networks | stat.ML | Machine learning approaches hold great potential for the automated detection
of lung nodules in chest radiographs, but training the algorithms requires vary
large amounts of manually annotated images, which are difficult to obtain. Weak
labels indicating whether a radiograph is likely to contain pulmonary nodules
are t... | computer science |
4,457 | Manifold-valued Image Generation with Wasserstein Adversarial Networks | cs.CV | Unsupervised image generation has recently received an increasing amount of
attention thanks to the great success of generative adversarial networks
(GANs), particularly Wasserstein GANs. Inspired by the paradigm of real-valued
image generation, this paper makes the first attempt to formulate the problem
of generating ... | computer science |
4,458 | Using SVDD in SimpleMKL for 3D-Shapes Filtering | stat.ML | This paper proposes the adaptation of Support Vector Data Description (SVDD)
to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a
variant called Slim-MK-SVDD that is able to produce a tighter frontier around
the data. For the sake of comparison, the equivalent methods are also developed
for O... | computer science |
4,459 | Panoramic Robust PCA for Foreground-Background Separation on Noisy,
Free-Motion Camera Video | stat.ML | This work presents a novel approach for robust PCA with total variation
regularization for foreground-background separation and denoising on noisy,
moving camera video. Our proposed algorithm registers the raw (possibly
corrupted) frames of a video and then jointly processes the registered frames
to produce a decomposi... | computer science |
4,460 | Deformable Classifiers | stat.ML | Geometric variations of objects, which do not modify the object class, pose a
major challenge for object recognition. These variations could be rigid as well
as non-rigid transformations. In this paper, we design a framework for training
deformable classifiers, where latent transformation variables are introduced,
and ... | computer science |
4,461 | Automatic Renal Segmentation in DCE-MRI using Convolutional Neural
Networks | cs.CV | Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)
images could help in diagnosis and treatment of kidney diseases of children.
Automatic segmentation of renal parenchyma is an important step in this
process. In this paper, we propose a time and memory efficient fully automated
segmentation method... | computer science |
4,462 | Deep metric learning for multi-labelled radiographs | stat.ML | Many radiological studies can reveal the presence of several co-existing
abnormalities, each one represented by a distinct visual pattern. In this
article we address the problem of learning a distance metric for plain
radiographs that captures a notion of "radiological similarity": two chest
radiographs are considered ... | computer science |
4,463 | Detection and segmentation of the Left Ventricle in Cardiac MRI using
Deep Learning | cs.CV | Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous
task that can vary depending on the patient, the Magnetic Resonance Images
(MRI) cuts and the experts. Still today, we consider manual delineation done by
experts as being the ground truth for cardiac diagnosticians. Thus, we are
reviewing the p... | computer science |
4,464 | Image denoising and restoration with CNN-LSTM Encoder Decoder with
Direct Attention | stat.ML | Image denoising is always a challenging task in the field of computer vision
and image processing. In this paper, we have proposed an encoder-decoder model
with direct attention, which is capable of denoising and reconstruct highly
corrupted images. Our model consists of an encoder and a decoder, where the
encoder is a... | computer science |
4,465 | Faster gaze prediction with dense networks and Fisher pruning | cs.CV | Predicting human fixations from images has recently seen large improvements
by leveraging deep representations which were pretrained for object
recognition. However, as we show in this paper, these networks are highly
overparameterized for the task of fixation prediction. We first present a
simple yet principled greedy... | computer science |
4,466 | What Makes Good Synthetic Training Data for Learning Disparity and
Optical Flow Estimation? | cs.CV | The finding that very large networks can be trained efficiently and reliably
has led to a paradigm shift in computer vision from engineered solutions to
learning formulations. As a result, the research challenge shifts from devising
algorithms to creating suitable and abundant training data for supervised
learning. How... | computer science |
4,467 | Detecting and counting tiny faces | cs.CV | Finding Tiny Faces (by Hu and Ramanan) proposes a novel approach to find
small objects in an image. Our contribution consists in deeply understanding
the choices of the paper together with applying and extending a similar method
to a real world subject which is the counting of people in a public
demonstration. | computer science |
4,468 | Depth CNNs for RGB-D scene recognition: learning from scratch better
than transferring from RGB-CNNs | cs.CV | Scene recognition with RGB images has been extensively studied and has
reached very remarkable recognition levels, thanks to convolutional neural
networks (CNN) and large scene datasets. In contrast, current RGB-D scene data
is much more limited, so often leverages RGB large datasets, by transferring
pretrained RGB CNN... | computer science |
4,469 | ClassSim: Similarity between Classes Defined by Misclassification Ratios
of Trained Classifiers | cs.CV | Deep neural networks (DNNs) have achieved exceptional performances in many
tasks, particularly, in supervised classification tasks. However, achievements
with supervised classification tasks are based on large datasets with
well-separated classes. Typically, real-world applications involve wild
datasets that include si... | computer science |
4,470 | An Occluded Stacked Hourglass Approach to Facial Landmark Localization
and Occlusion Estimation | cs.CV | A key step to driver safety is to observe the driver's activities with the
face being a key step in this process to extracting information such as head
pose, blink rate, yawns, talking to passenger which can then help derive higher
level information such as distraction, drowsiness, intent, and where they are
looking. I... | computer science |
4,471 | Generative Adversarial Networks using Adaptive Convolution | cs.CV | Most existing GANs architectures that generate images use transposed
convolution or resize-convolution as their upsampling algorithm from lower to
higher resolution feature maps in the generator. We argue that this kind of
fixed operation is problematic for GANs to model objects that have very
different visual appearan... | computer science |
4,472 | Scalable and Robust Sparse Subspace Clustering Using Randomized
Clustering and Multilayer Graphs | cs.CV | Sparse subspace clustering (SSC) is one of the current state-of-the-art
methods for partitioning data points into the union of subspaces, with strong
theoretical guarantees. However, it is not practical for large data sets as it
requires solving a LASSO problem for each data point, where the number of
variables in each... | computer science |
4,473 | Training Deep Learning based Denoisers without Ground Truth Data | cs.CV | Recent deep learning based denoisers are trained to minimize the mean squared
error (MSE) between the output of a network and the ground truth noiseless
image in the training data. Thus, it is crucial to have high quality noiseless
training data for high performance denoisers. Unfortunately, in some
application areas s... | computer science |
4,474 | Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration | cs.CV | Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) ... | computer science |
4,475 | TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree
Search | cs.LG | In this paper we present TDLeaf(lambda), a variation on the TD(lambda)
algorithm that enables it to be used in conjunction with minimax search. We
present some experiments in both chess and backgammon which demonstrate its
utility and provide comparisons with TD(lambda) and another less radical
variant, TD-directed(lam... | computer science |
4,476 | KnightCap: A chess program that learns by combining TD(lambda) with
game-tree search | cs.LG | In this paper we present TDLeaf(lambda), a variation on the TD(lambda)
algorithm that enables it to be used in conjunction with game-tree search. We
present some experiments in which our chess program ``KnightCap'' used
TDLeaf(lambda) to learn its evaluation function while playing on the Free
Internet Chess Server (FIC... | computer science |
4,477 | Automatically Selecting Useful Phrases for Dialogue Act Tagging | cs.AI | We present an empirical investigation of various ways to automatically
identify phrases in a tagged corpus that are useful for dialogue act tagging.
We found that a new method (which measures a phrase's deviation from an
optimally-predictive phrase), enhanced with a lexical filtering mechanism,
produces significantly b... | computer science |
4,478 | New Error Bounds for Solomonoff Prediction | cs.AI | Solomonoff sequence prediction is a scheme to predict digits of binary
strings without knowing the underlying probability distribution. We call a
prediction scheme informed when it knows the true probability distribution of
the sequence. Several new relations between universal Solomonoff sequence
prediction and informe... | computer science |
4,479 | Modeling the Uncertainty in Complex Engineering Systems | cs.AI | Existing procedures for model validation have been deemed inadequate for many
engineering systems. The reason of this inadequacy is due to the high degree of
complexity of the mechanisms that govern these systems. It is proposed in this
paper to shift the attention from modeling the engineering system itself to
modelin... | computer science |
4,480 | Relevant Knowledge First - Reinforcement Learning and Forgetting in
Knowledge Based Configuration | cs.AI | In order to solve complex configuration tasks in technical domains, various
knowledge based methods have been developed. However their applicability is
often unsuccessful due to their low efficiency. One of the reasons for this is
that (parts of the) problems have to be solved again and again, instead of
being "learnt"... | computer science |
4,481 | Robust Feature Selection by Mutual Information Distributions | cs.AI | Mutual information is widely used in artificial intelligence, in a
descriptive way, to measure the stochastic dependence of discrete random
variables. In order to address questions such as the reliability of the
empirical value, one must consider sample-to-population inferential approaches.
This paper deals with the di... | computer science |
4,482 | The Prioritized Inductive Logic Programs | cs.AI | The limit behavior of inductive logic programs has not been explored, but
when considering incremental or online inductive learning algorithms which
usually run ongoingly, such behavior of the programs should be taken into
account. An example is given to show that some inductive learning algorithm may
not be correct in... | computer science |
4,483 | Maximing the Margin in the Input Space | cs.AI | We propose a novel criterion for support vector machine learning: maximizing
the margin in the input space, not in the feature (Hilbert) space. This
criterion is a discriminative version of the principal curve proposed by Hastie
et al. The criterion is appropriate in particular when the input space is
already a well-de... | computer science |
4,484 | Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent
Space Alignment | cs.LG | Nonlinear manifold learning from unorganized data points is a very
challenging unsupervised learning and data visualization problem with a great
variety of applications. In this paper we present a new algorithm for manifold
learning and nonlinear dimension reduction. Based on a set of unorganized data
points sampled wi... | computer science |
4,485 | Kalman filter control in the reinforcement learning framework | cs.LG | There is a growing interest in using Kalman-filter models in brain modelling.
In turn, it is of considerable importance to make Kalman-filters amenable for
reinforcement learning. In the usual formulation of optimal control it is
computed off-line by solving a backward recursion. In this technical note we
show that sli... | computer science |
4,486 | Unsupervised Learning in a Framework of Information Compression by
Multiple Alignment, Unification and Search | cs.AI | This paper describes a novel approach to unsupervised learning that has been
developed within a framework of "information compression by multiple alignment,
unification and search" (ICMAUS), designed to integrate learning with other AI
functions such as parsing and production of language, fuzzy pattern
recognition, pro... | computer science |
4,487 | Reinforcement Learning with Linear Function Approximation and LQ control
Converges | cs.LG | Reinforcement learning is commonly used with function approximation. However,
very few positive results are known about the convergence of function
approximation based RL control algorithms. In this paper we show that TD(0) and
Sarsa(0) with linear function approximation is convergent for a simple class of
problems, wh... | computer science |
4,488 | Concept of E-machine: How does a "dynamical" brain learn to process
"symbolic" information? Part I | cs.AI | The human brain has many remarkable information processing characteristics
that deeply puzzle scientists and engineers. Among the most important and the
most intriguing of these characteristics are the brain's broad universality as
a learning system and its mysterious ability to dynamically change
(reconfigure) its beh... | computer science |
4,489 | Tournament versus Fitness Uniform Selection | cs.LG | In evolutionary algorithms a critical parameter that must be tuned is that of
selection pressure. If it is set too low then the rate of convergence towards
the optimum is likely to be slow. Alternatively if the selection pressure is
set too high the system is likely to become stuck in a local optimum due to a
loss of d... | computer science |
4,490 | Prediction with Expert Advice by Following the Perturbed Leader for
General Weights | cs.LG | When applying aggregating strategies to Prediction with Expert Advice, the
learning rate must be adaptively tuned. The natural choice of
sqrt(complexity/current loss) renders the analysis of Weighted Majority
derivatives quite complicated. In particular, for arbitrary weights there have
been no results proven so far. T... | computer science |
4,491 | Learning for Adaptive Real-time Search | cs.AI | Real-time heuristic search is a popular model of acting and learning in
intelligent autonomous agents. Learning real-time search agents improve their
performance over time by acquiring and refining a value function guiding the
application of their actions. As computing the perfect value function is
typically intractabl... | computer science |
4,492 | L1 regularization is better than L2 for learning and predicting chaotic
systems | cs.LG | Emergent behaviors are in the focus of recent research interest. It is then
of considerable importance to investigate what optimizations suit the learning
and prediction of chaotic systems, the putative candidates for emergence. We
have compared L1 and L2 regularizations on predicting chaotic time series using
linear r... | computer science |
4,493 | A Note on the PAC Bayesian Theorem | cs.LG | We prove general exponential moment inequalities for averages of [0,1]-valued
iid random variables and use them to tighten the PAC Bayesian Theorem. The
logarithmic dependence on the sample count in the enumerator of the PAC
Bayesian bound is halved. | computer science |
4,494 | Master Algorithms for Active Experts Problems based on Increasing Loss
Values | cs.LG | We specify an experts algorithm with the following characteristics: (a) it
uses only feedback from the actions actually chosen (bandit setup), (b) it can
be applied with countably infinite expert classes, and (c) it copes with losses
that may grow in time appropriately slowly. We prove loss bounds against an
adaptive a... | computer science |
4,495 | The Bayesian Decision Tree Technique with a Sweeping Strategy | cs.AI | The uncertainty of classification outcomes is of crucial importance for many
safety critical applications including, for example, medical diagnostics. In
such applications the uncertainty of classification can be reliably estimated
within a Bayesian model averaging technique that allows the use of prior
information. De... | computer science |
4,496 | Experimental Comparison of Classification Uncertainty for Randomised and
Bayesian Decision Tree Ensembles | cs.AI | In this paper we experimentally compare the classification uncertainty of the
randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique
with a restarting strategy on a synthetic dataset as well as on some datasets
commonly used in the machine learning community. For quantitative evaluation of
class... | computer science |
4,497 | Adaptive Online Prediction by Following the Perturbed Leader | cs.AI | When applying aggregating strategies to Prediction with Expert Advice, the
learning rate must be adaptively tuned. The natural choice of
sqrt(complexity/current loss) renders the analysis of Weighted Majority
derivatives quite complicated. In particular, for arbitrary weights there have
been no results proven so far. T... | computer science |
4,498 | Componentwise Least Squares Support Vector Machines | cs.LG | This chapter describes componentwise Least Squares Support Vector Machines
(LS-SVMs) for the estimation of additive models consisting of a sum of
nonlinear components. The primal-dual derivations characterizing LS-SVMs for
the estimation of the additive model result in a single set of linear equations
with size growing... | computer science |
4,499 | Defensive forecasting | cs.LG | We consider how to make probability forecasts of binary labels. Our main
mathematical result is that for any continuous gambling strategy used for
detecting disagreement between the forecasts and the actual labels, there
exists a forecasting strategy whose forecasts are ideal as far as this gambling
strategy is concern... | computer science |
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