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8,500 | Segmentation of optic disc, fovea and retinal vasculature using a single
convolutional neural network | cs.CV | We have developed and trained a convolutional neural network to automatically
and simultaneously segment optic disc, fovea and blood vessels. Fundus images
were normalised before segmentation was performed to enforce consistency in
background lighting and contrast. For every effective point in the fundus
image, our alg... | computer science |
8,501 | HashNet: Deep Learning to Hash by Continuation | cs.LG | Learning to hash has been widely applied to approximate nearest neighbor
search for large-scale multimedia retrieval, due to its computation efficiency
and retrieval quality. Deep learning to hash, which improves retrieval quality
by end-to-end representation learning and hash encoding, has received
increasing attentio... | computer science |
8,502 | Pixel Recursive Super Resolution | cs.CV | We present a pixel recursive super resolution model that synthesizes
realistic details into images while enhancing their resolution. A low
resolution image may correspond to multiple plausible high resolution images,
thus modeling the super resolution process with a pixel independent conditional
model often results in ... | computer science |
8,503 | Intrinsic Grassmann Averages for Online Linear and Robust Subspace
Learning | cs.LG | Principal Component Analysis (PCA) is a fundamental method for estimating a
linear subspace approximation to high-dimensional data. Many algorithms exist
in literature to achieve a statistically robust version of PCA called RPCA. In
this paper, we present a geometric framework for computing the principal linear
subspac... | computer science |
8,504 | Latent Hinge-Minimax Risk Minimization for Inference from a Small Number
of Training Samples | cs.LG | 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 settin... | computer science |
8,505 | Joint Discovery of Object States and Manipulation Actions | cs.CV | Many human activities involve object manipulations aiming to modify the
object state. Examples of common state changes include full/empty bottle,
open/closed door, and attached/detached car wheel. In this work, we seek to
automatically discover the states of objects and the associated manipulation
actions. Given a set ... | computer science |
8,506 | Discovering objects and their relations from entangled scene
representations | cs.LG | Our world can be succinctly and compactly described as structured scenes of
objects and relations. A typical room, for example, contains salient objects
such as tables, chairs and books, and these objects typically relate to each
other by their underlying causes and semantics. This gives rise to correlated
features, su... | computer science |
8,507 | The importance of stain normalization in colorectal tissue
classification with convolutional networks | cs.CV | The development of reliable imaging biomarkers for the analysis of colorectal
cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images
requires an accurate and reproducible classification of the main tissue
components in the image. In this paper, we propose a system for CRC tissue
classification based ... | computer science |
8,508 | An Extended Framework for Marginalized Domain Adaptation | cs.CV | We propose an extended framework for marginalized domain adaptation, aimed at
addressing unsupervised, supervised and semi-supervised scenarios. We argue
that the denoising principle should be extended to explicitly promote
domain-invariant features as well as help the classification task. Therefore we
propose to joint... | computer science |
8,509 | Label Distribution Learning Forests | cs.LG | Label distribution learning (LDL) is a general learning framework, which
assigns to an instance a distribution over a set of labels rather than a single
label or multiple labels. Current LDL methods have either restricted
assumptions on the expression form of the label distribution or limitations in
representation lear... | computer science |
8,510 | Scene Recognition by Combining Local and Global Image Descriptors | cs.CV | Object recognition is an important problem in computer vision, having diverse
applications. In this work, we construct an end-to-end scene recognition
pipeline consisting of feature extraction, encoding, pooling and
classification. Our approach simultaneously utilize global feature descriptors
as well as local feature ... | computer science |
8,511 | How ConvNets model Non-linear Transformations | cs.CV | In this paper, we theoretically address three fundamental problems involving
deep convolutional networks regarding invariance, depth and hierarchy. We
introduce the paradigm of Transformation Networks (TN) which are a direct
generalization of Convolutional Networks (ConvNets). Theoretically, we show
that TNs (and there... | computer science |
8,512 | Bayesian Nonparametric Feature and Policy Learning for Decision-Making | cs.LG | Learning from demonstrations has gained increasing interest in the recent
past, enabling an agent to learn how to make decisions by observing an
experienced teacher. While many approaches have been proposed to solve this
problem, there is only little work that focuses on reasoning about the observed
behavior. We assume... | computer science |
8,513 | ShaResNet: reducing residual network parameter number by sharing weights | cs.CV | Deep Residual Networks have reached the state of the art in many image
processing tasks such image classification. However, the cost for a gain in
accuracy in terms of depth and memory is prohibitive as it requires a higher
number of residual blocks, up to double the initial value. To tackle this
problem, we propose in... | computer science |
8,514 | Graph-based Isometry Invariant Representation Learning | cs.CV | Learning transformation invariant representations of visual data is an
important problem in computer vision. Deep convolutional networks have
demonstrated remarkable results for image and video classification tasks.
However, they have achieved only limited success in the classification of
images that undergo geometric ... | computer science |
8,515 | Wireless Interference Identification with Convolutional Neural Networks | cs.LG | The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural network... | computer science |
8,516 | Attentive Recurrent Comparators | cs.CV | Rapid learning requires flexible representations to quickly adopt to new
evidence. We develop a novel class of models called Attentive Recurrent
Comparators (ARCs) that form representations of objects by cycling through them
and making observations. Using the representations extracted by ARCs, we
develop a way of appro... | computer science |
8,517 | LR-GAN: Layered Recursive Generative Adversarial Networks for Image
Generation | cs.CV | We present LR-GAN: an adversarial image generation model which takes scene
structure and context into account. Unlike previous generative adversarial
networks (GANs), the proposed GAN learns to generate image background and
foregrounds separately and recursively, and stitch the foregrounds on the
background in a contex... | computer science |
8,518 | Building a Regular Decision Boundary with Deep Networks | cs.CV | In this work, we build a generic architecture of Convolutional Neural
Networks to discover empirical properties of neural networks. Our first
contribution is to introduce a state-of-the-art framework that depends upon few
hyper parameters and to study the network when we vary them. It has no max
pooling, no biases, onl... | computer science |
8,519 | Distance Metric Learning using Graph Convolutional Networks: Application
to Functional Brain Networks | cs.CV | Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the... | computer science |
8,520 | Triple Generative Adversarial Nets | cs.LG | Generative Adversarial Nets (GANs) have shown promise in image generation and
semi-supervised learning (SSL). However, existing GANs in SSL have two
problems: (1) the generator and the discriminator (i.e. the classifier) may not
be optimal at the same time; and (2) the generator cannot control the semantics
of the gene... | computer science |
8,521 | Qualitative Assessment of Recurrent Human Motion | cs.LG | Smartphone applications designed to track human motion in combination with
wearable sensors, e.g., during physical exercising, raised huge attention
recently. Commonly, they provide quantitative services, such as personalized
training instructions or the counting of distances. But qualitative monitoring
and assessment ... | computer science |
8,522 | A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile
Analytics | cs.LG | The increasing quality of smartphone cameras and variety of photo editing
applications, in addition to the rise in popularity of image-centric social
media, have all led to a phenomenal growth in mobile-based photography.
Advances in computer vision and machine learning techniques provide a large
number of cloud-based ... | computer science |
8,523 | Deep Convolutional Neural Network Inference with Floating-point Weights
and Fixed-point Activations | cs.LG | Deep convolutional neural network (CNN) inference requires significant amount
of memory and computation, which limits its deployment on embedded devices. To
alleviate these problems to some extent, prior research utilize low precision
fixed-point numbers to represent the CNN weights and activations. However, the
minimu... | computer science |
8,524 | Convolutional neural network architecture for geometric matching | cs.CV | We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric ma... | computer science |
8,525 | Automatically identifying, counting, and describing wild animals in
camera-trap images with deep learning | cs.CV | Having accurate, detailed, and up-to-date information about the location and
behavior of animals in the wild would revolutionize our ability to study and
conserve ecosystems. We investigate the ability to automatically, accurately,
and inexpensively collect such data, which could transform many fields of
biology, ecolo... | computer science |
8,526 | Unsupervised Anomaly Detection with Generative Adversarial Networks to
Guide Marker Discovery | cs.CV | Obtaining models that capture imaging markers relevant for disease
progression and treatment monitoring is challenging. Models are typically based
on large amounts of data with annotated examples of known markers aiming at
automating detection. High annotation effort and the limitation to a vocabulary
of known markers ... | computer science |
8,527 | Comparison of Different Methods for Tissue Segmentation in
Histopathological Whole-Slide Images | cs.CV | Tissue segmentation is an important pre-requisite for efficient and accurate
diagnostics in digital pathology. However, it is well known that whole-slide
scanners can fail in detecting all tissue regions, for example due to the
tissue type, or due to weak staining because their tissue detection algorithms
are not robus... | computer science |
8,528 | Active Decision Boundary Annotation with Deep Generative Models | cs.CV | This paper is on active learning where the goal is to reduce the data
annotation burden by interacting with a (human) oracle during training.
Standard active learning methods ask the oracle to annotate data samples.
Instead, we take a profoundly different approach: we ask for annotations of the
decision boundary. We ac... | computer science |
8,529 | How far are we from solving the 2D & 3D Face Alignment problem? (and a
dataset of 230,000 3D facial landmarks) | cs.CV | This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for la... | computer science |
8,530 | Predicting Deeper into the Future of Semantic Segmentation | cs.CV | The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future vi... | computer science |
8,531 | Discriminatively Boosted Image Clustering with Fully Convolutional
Auto-Encoders | cs.CV | Traditional image clustering methods take a two-step approach, feature
learning and clustering, sequentially. However, recent research results
demonstrated that combining the separated phases in a unified framework and
training them jointly can achieve a better performance. In this paper, we first
introduce fully convo... | computer science |
8,532 | On the Robustness of Convolutional Neural Networks to Internal
Architecture and Weight Perturbations | cs.LG | Deep convolutional neural networks are generally regarded as robust function
approximators. So far, this intuition is based on perturbations to external
stimuli such as the images to be classified. Here we explore the robustness of
convolutional neural networks to perturbations to the internal weights and
architecture ... | computer science |
8,533 | Who Said What: Modeling Individual Labelers Improves Classification | cs.LG | Data are often labeled by many different experts with each expert only
labeling a small fraction of the data and each data point being labeled by
several experts. This reduces the workload on individual experts and also gives
a better estimate of the unobserved ground truth. When experts disagree, the
standard approach... | computer science |
8,534 | Learned Multi-Patch Similarity | cs.CV | Estimating a depth map from multiple views of a scene is a fundamental task
in computer vision. As soon as more than two viewpoints are available, one
faces the very basic question how to measure similarity across >2 image
patches. Surprisingly, no direct solution exists, instead it is common to fall
back to more or le... | computer science |
8,535 | Scaling the Scattering Transform: Deep Hybrid Networks | cs.CV | We use the scattering network as a generic and fixed ini-tialization of the
first layers of a supervised hybrid deep network. We show that early layers do
not necessarily need to be learned, providing the best results to-date with
pre-defined representations while being competitive with Deep CNNs. Using a
shallow casca... | computer science |
8,536 | Learned Spectral Super-Resolution | cs.CV | We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hy... | computer science |
8,537 | Two-Stream RNN/CNN for Action Recognition in 3D Videos | cs.CV | The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system whi... | computer science |
8,538 | Deceiving Google's Cloud Video Intelligence API Built for Summarizing
Videos | cs.CV | Despite the rapid progress of the techniques for image classification, video
annotation has remained a challenging task. Automated video annotation would be
a breakthrough technology, enabling users to search within the videos.
Recently, Google introduced the Cloud Video Intelligence API for video
analysis. As per the ... | computer science |
8,539 | Interpretable Learning for Self-Driving Cars by Visualizing Causal
Attention | cs.CV | Deep neural perception and control networks are likely to be a key component
of self-driving vehicles. These models need to be explainable - they should
provide easy-to-interpret rationales for their behavior - so that passengers,
insurance companies, law enforcement, developers etc., can understand what
triggered a pa... | computer science |
8,540 | SafetyNet: Detecting and Rejecting Adversarial Examples Robustly | cs.CV | We describe a method to produce a network where current methods such as
DeepFool have great difficulty producing adversarial samples. Our construction
suggests some insights into how deep networks work. We provide a reasonable
analyses that our construction is difficult to defeat, and show experimentally
that our metho... | computer science |
8,541 | Clustering in Hilbert simplex geometry | cs.LG | Clustering categorical distributions in the probability simplex is a
fundamental primitive often met in applications dealing with histograms or
mixtures of multinomials. Traditionally, the differential-geometric structure
of the probability simplex has been used either by (i) setting the Riemannian
metric tensor to the... | computer science |
8,542 | Soft-to-Hard Vector Quantization for End-to-End Learning Compressible
Representations | cs.LG | We present a new approach to learn compressible representations in deep
architectures with an end-to-end training strategy. Our method is based on a
soft (continuous) relaxation of quantization and entropy, which we anneal to
their discrete counterparts throughout training. We showcase this method for
two challenging a... | computer science |
8,543 | Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via
Deep Layer Cascade | cs.CV | We propose a novel deep layer cascade (LC) method to improve the accuracy and
speed of semantic segmentation. Unlike the conventional model cascade (MC) that
is composed of multiple independent models, LC treats a single deep model as a
cascade of several sub-models. Earlier sub-models are trained to handle easy
and co... | computer science |
8,544 | Automatic Breast Ultrasound Image Segmentation: A Survey | cs.CV | Breast cancer is one of the leading causes of cancer death among women
worldwide. In clinical routine, automatic breast ultrasound (BUS) image
segmentation is very challenging and essential for cancer diagnosis and
treatment planning. Many BUS segmentation approaches have been studied in the
last two decades, and have ... | computer science |
8,545 | Online Hashing | cs.CV | Although hash function learning algorithms have achieved great success in
recent years, most existing hash models are off-line, which are not suitable
for processing sequential or online data. To address this problem, this work
proposes an online hash model to accommodate data coming in stream for online
learning. Spec... | computer science |
8,546 | Land Cover Classification via Multi-temporal Spatial Data by Recurrent
Neural Networks | cs.CV | Nowadays, modern earth observation programs produce huge volumes of satellite
images time series (SITS) that can be useful to monitor geographical areas
through time. How to efficiently analyze such kind of information is still an
open question in the remote sensing field. Recently, deep learning methods
proved suitabl... | computer science |
8,547 | Learning a collaborative multiscale dictionary based on robust empirical
mode decomposition | cs.CV | Dictionary learning is a challenge topic in many image processing areas. The
basic goal is to learn a sparse representation from an overcomplete basis set.
Due to combining the advantages of generic multiscale representations with
learning based adaptivity, multiscale dictionary representation approaches have
the power... | computer science |
8,548 | Deep Learning for Photoacoustic Tomography from Sparse Data | cs.CV | The development of fast and accurate image reconstruction algorithms is a
central aspect of computed tomography. In this paper we investigate this issue
for the sparse data problem in photoacoustic tomography (PAT). We develop a
direct and highly efficient reconstruction algorithm based on deep learning. In
our approac... | computer science |
8,549 | Gang of GANs: Generative Adversarial Networks with Maximum Margin
Ranking | cs.CV | Traditional generative adversarial networks (GAN) and many of its variants
are trained by minimizing the KL or JS-divergence loss that measures how close
the generated data distribution is from the true data distribution. A recent
advance called the WGAN based on Wasserstein distance can improve on the KL and
JS-diverg... | computer science |
8,550 | Google's Cloud Vision API Is Not Robust To Noise | cs.CV | Google has recently introduced the Cloud Vision API for image analysis.
According to the demonstration website, the API "quickly classifies images into
thousands of categories, detects individual objects and faces within images,
and finds and reads printed words contained within images." It can be also used
to "detect ... | computer science |
8,551 | Insensitive Stochastic Gradient Twin Support Vector Machine for Large
Scale Problems | cs.LG | Stochastic gradient descent algorithm has been successfully applied on
support vector machines (called PEGASOS) for many classification problems. In
this paper, stochastic gradient descent algorithm is investigated to twin
support vector machines for classification. Compared with PEGASOS, the proposed
stochastic gradie... | computer science |
8,552 | Unsupervised Creation of Parameterized Avatars | cs.CV | We study the problem of mapping an input image to a tied pair consisting of a
vector of parameters and an image that is created using a graphical engine from
the vector of parameters. The mapping's objective is to have the output image
as similar as possible to the input image. During training, no supervision is
given ... | computer science |
8,553 | A Deep Learning Framework using Passive WiFi Sensing for Respiration
Monitoring | cs.CV | This paper presents an end-to-end deep learning framework using passive WiFi
sensing to classify and estimate human respiration activity. A passive radar
test-bed is used with two channels where the first channel provides the
reference WiFi signal, whereas the other channel provides a surveillance signal
that contains ... | computer science |
8,554 | End-to-end representation learning for Correlation Filter based tracking | cs.CV | The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Corr... | computer science |
8,555 | Multimodal MRI brain tumor segmentation using random forests with
features learned from fully convolutional neural network | cs.CV | In this paper, we propose a novel learning based method for automated
segmenta-tion of brain tumor in multimodal MRI images. The machine learned
features from fully convolutional neural network (FCN) and hand-designed texton
fea-tures are used to classify the MRI image voxels. The score map with
pixel-wise predictions ... | computer science |
8,556 | Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task
Learning | cs.CV | Risk stratification of lung nodules is a task of primary importance in lung
cancer diagnosis. Any improvement in robust and accurate nodule
characterization can assist in identifying cancer stage, prognosis, and
improving treatment planning. In this study, we propose a 3D Convolutional
Neural Network (CNN) based nodule... | computer science |
8,557 | Deep Multi-view Models for Glitch Classification | cs.LG | Non-cosmic, non-Gaussian disturbances known as "glitches", show up in
gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave
Observatory, or aLIGO. In this paper, we propose a deep multi-view
convolutional neural network to classify glitches automatically. The primary
purpose of classifying gli... | computer science |
8,558 | Regularized Residual Quantization: a multi-layer sparse dictionary
learning approach | cs.LG | The Residual Quantization (RQ) framework is revisited where the quantization
distortion is being successively reduced in multi-layers. Inspired by the
reverse-water-filling paradigm in rate-distortion theory, an efficient
regularization on the variances of the codewords is introduced which allows to
extend the RQ for v... | computer science |
8,559 | Single image depth estimation by dilated deep residual convolutional
neural network and soft-weight-sum inference | cs.CV | This paper proposes a new residual convolutional neural network (CNN)
architecture for single image depth estimation. Compared with existing deep CNN
based methods, our method achieves much better results with fewer training
examples and model parameters. The advantages of our method come from the usage
of dilated conv... | computer science |
8,560 | A Strategy for an Uncompromising Incremental Learner | cs.CV | Multi-class supervised learning systems require the knowledge of the entire
range of labels they predict. Often when learnt incrementally, they suffer from
catastrophic forgetting. To avoid this, generous leeways have to be made to the
philosophy of incremental learning that either forces a part of the machine to
not l... | computer science |
8,561 | Generative Convolutional Networks for Latent Fingerprint Reconstruction | cs.CV | Performance of fingerprint recognition depends heavily on the extraction of
minutiae points. Enhancement of the fingerprint ridge pattern is thus an
essential pre-processing step that noticeably reduces false positive and
negative detection rates. A particularly challenging setting is when the
fingerprint images are co... | computer science |
8,562 | Deep Descriptor Transforming for Image Co-Localization | cs.CV | Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i... | computer science |
8,563 | Collaborative Descriptors: Convolutional Maps for Preprocessing | cs.CV | The paper presents a novel concept for collaborative descriptors between
deeply learned and hand-crafted features. To achieve this concept, we apply
convolutional maps for pre-processing, namely the convovlutional maps are used
as input of hand-crafted features. We recorded an increase in the performance
rate of +17.06... | computer science |
8,564 | Incremental Learning Through Deep Adaptation | cs.CV | Given an existing trained neural network, it is often desirable to learn new
capabilities without hindering performance of those already learned. Existing
approaches either learn sub-optimal solutions, require joint training, or incur
a substantial increment in the number of parameters for each added domain,
typically ... | computer science |
8,565 | Learning Image Relations with Contrast Association Networks | cs.CV | Inferring the relations between two images is an important class of tasks in
computer vision. Examples of such tasks include computing optical flow and
stereo disparity. We treat the relation inference tasks as a machine learning
problem and tackle it with neural networks. A key to the problem is learning a
representat... | computer science |
8,566 | One Shot Joint Colocalization and Cosegmentation | cs.CV | This paper presents a novel framework in which image cosegmentation and
colocalization are cast into a single optimization problem that integrates
information from low level appearance cues with that of high level localization
cues in a very weakly supervised manner. In contrast to multi-task learning
paradigm that lea... | computer science |
8,567 | What do We Learn by Semantic Scene Understanding for Remote Sensing
imagery in CNN framework? | cs.CV | Recently, deep convolutional neural network (DCNN) achieved increasingly
remarkable success and rapidly developed in the field of natural image
recognition. Compared with the natural image, the scale of remote sensing image
is larger and the scene and the object it represents are more macroscopic. This
study inquires w... | computer science |
8,568 | Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image
Generation | cs.CV | Variational auto-encoder (VAE) is a powerful unsupervised learning framework
for image generation. One drawback of VAE is that it generates blurry images
due to its Gaussianity assumption and thus L2 loss. To allow the generation of
high quality images by VAE, we increase the capacity of decoder network by
employing re... | computer science |
8,569 | PixColor: Pixel Recursive Colorization | cs.CV | We propose a novel approach to automatically produce multiple colorized
versions of a grayscale image. Our method results from the observation that the
task of automated colorization is relatively easy given a low-resolution
version of the color image. We first train a conditional PixelCNN to generate a
low resolution ... | computer science |
8,570 | CrossNets : A New Approach to Complex Learning | cs.CV | We propose a novel neural network structure called CrossNets, which considers
architectures on directed acyclic graphs. This structure builds on previous
generalizations of feed forward models, such as ResNets, by allowing for all
forward cross connections between layers (both adjacent and non-adjacent). The
addition o... | computer science |
8,571 | Shake-Shake regularization | cs.LG | The method introduced in this paper aims at helping deep learning
practitioners faced with an overfit problem. The idea is to replace, in a
multi-branch network, the standard summation of parallel branches with a
stochastic affine combination. Applied to 3-branch residual networks,
shake-shake regularization improves o... | computer science |
8,572 | Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset | cs.CV | The paucity of videos in current action classification datasets (UCF-101 and
HMDB-51) has made it difficult to identify good video architectures, as most
methods obtain similar performance on existing small-scale benchmarks. This
paper re-evaluates state-of-the-art architectures in light of the new Kinetics
Human Actio... | computer science |
8,573 | Stabilizing GAN Training with Multiple Random Projections | cs.LG | Training generative adversarial networks is unstable in high-dimensions when
the true data distribution lies on a lower-dimensional manifold. The
discriminator is then easily able to separate nearly all generated samples
leaving the generator without meaningful gradients. We propose training a
single generator simultan... | computer science |
8,574 | Look, Listen and Learn | cs.CV | We consider the question: what can be learnt by looking at and listening to a
large number of unlabelled videos? There is a valuable, but so far untapped,
source of information contained in the video itself -- the correspondence
between the visual and the audio streams, and we introduce a novel
"Audio-Visual Correspond... | computer science |
8,575 | Stochastic Sequential Neural Networks with Structured Inference | cs.LG | Unsupervised structure learning in high-dimensional time series data has
attracted a lot of research interests. For example, segmenting and labelling
high dimensional time series can be helpful in behavior understanding and
medical diagnosis. Recent advances in generative sequential modeling have
suggested to combine r... | computer science |
8,576 | Classification of Quantitative Light-Induced Fluorescence Images Using
Convolutional Neural Network | cs.CV | Images are an important data source for diagnosis and treatment of oral
diseases. The manual classification of images may lead to misdiagnosis or
mistreatment due to subjective errors. In this paper an image classification
model based on Convolutional Neural Network is applied to Quantitative
Light-induced Fluorescence... | computer science |
8,577 | Learning Robust Features with Incremental Auto-Encoders | cs.LG | Automatically learning features, especially robust features, has attracted
much attention in the machine learning community. In this paper, we propose a
new method to learn non-linear robust features by taking advantage of the data
manifold structure. We first follow the commonly used trick of the trade, that
is learni... | computer science |
8,578 | A Multi-strength Adversarial Training Method to Mitigate Adversarial
Attacks | cs.LG | Some recent works revealed that deep neural networks (DNNs) are vulnerable to
so-called adversarial attacks where input examples are intentionally perturbed
to fool DNNs. In this work, we revisit the DNN training process that includes
adversarial examples into the training dataset so as to improve DNN's
resilience to a... | computer science |
8,579 | Global hard thresholding algorithms for joint sparse image
representation and denoising | cs.CV | Sparse coding of images is traditionally done by cutting them into small
patches and representing each patch individually over some dictionary given a
pre-determined number of nonzero coefficients to use for each patch. In lack of
a way to effectively distribute a total number (or global budget) of nonzero
coefficients... | computer science |
8,580 | Multi-Focus Image Fusion Via Coupled Sparse Representation and
Dictionary Learning | cs.CV | We address the multi-focus image fusion problem, where multiple images
captured with different focal settings are to be fused into an all-in-focus
image of higher quality. Algorithms for this problem necessarily admit the
source image characteristics along with focused and blurred feature. However,
most sparsity-based ... | computer science |
8,581 | Personalized Pancreatic Tumor Growth Prediction via Group Learning | cs.CV | Tumor growth prediction, a highly challenging task, has long been viewed as a
mathematical modeling problem, where the tumor growth pattern is personalized
based on imaging and clinical data of a target patient. Though mathematical
models yield promising results, their prediction accuracy may be limited by the
absence ... | computer science |
8,582 | Convolutional Neural Networks for Medical Image Analysis: Full Training
or Fine Tuning? | cs.CV | Training a deep convolutional neural network (CNN) from scratch is difficult
because it requires a large amount of labeled training data and a great deal of
expertise to ensure proper convergence. A promising alternative is to fine-tune
a CNN that has been pre-trained using, for instance, a large set of labeled
natural... | computer science |
8,583 | Automating Carotid Intima-Media Thickness Video Interpretation with
Convolutional Neural Networks | cs.CV | Cardiovascular disease (CVD) is the leading cause of mortality yet largely
preventable, but the key to prevention is to identify at-risk individuals
before adverse events. For predicting individual CVD risk, carotid intima-media
thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable,
offering seve... | computer science |
8,584 | IDK Cascades: Fast Deep Learning by Learning not to Overthink | cs.CV | Advances in deep learning have led to substantial increases in prediction
accuracy but have been accompanied by increases in the cost of rendering
predictions. We conjecture that for a majority of real-world inputs, the recent
advances in deep learning have created models that effectively "over-think" on
simple inputs.... | computer science |
8,585 | Learning by Association - A versatile semi-supervised training method
for neural networks | cs.CV | In many real-world scenarios, labeled data for a specific machine learning
task is costly to obtain. Semi-supervised training methods make use of
abundantly available unlabeled data and a smaller number of labeled examples.
We propose a new framework for semi-supervised training of deep neural networks
inspired by lear... | computer science |
8,586 | Progressive Boosting for Class Imbalance | cs.LG | Pattern recognition applications often suffer from skewed data distributions
between classes, which may vary during operations w.r.t. the design data.
Two-class classification systems designed using skewed data tend to recognize
the majority class better than the minority class of interest. Several
data-level technique... | computer science |
8,587 | Learning to Extract Semantic Structure from Documents Using Multimodal
Fully Convolutional Neural Network | cs.CV | We present an end-to-end, multimodal, fully convolutional network for
extracting semantic structures from document images. We consider document
semantic structure extraction as a pixel-wise segmentation task, and propose a
unified model that classifies pixels based not only on their visual appearance,
as in the traditi... | computer science |
8,588 | Image Matching via Loopy RNN | cs.LG | Most existing matching algorithms are one-off algorithms, i.e., they usually
measure the distance between the two image feature representation vectors for
only one time. In contrast, human's vision system achieves this task, i.e.,
image matching, by recursively looking at specific/related parts of both images
and then ... | computer science |
8,589 | Enriched Deep Recurrent Visual Attention Model for Multiple Object
Recognition | cs.CV | We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) - an
improved attention-based architecture for multiple object recognition. The
proposed model is a fully differentiable unit that can be optimized end-to-end
by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was
employed as visu... | computer science |
8,590 | Channel-Recurrent Autoencoding for Image Modeling | cs.LG | Despite recent successes in synthesizing faces and bedrooms, existing
generative models struggle to capture more complex image types, potentially due
to the oversimplification of their latent space constructions. To tackle this
issue, building on Variational Autoencoders (VAEs), we integrate recurrent
connections acros... | computer science |
8,591 | SEP-Nets: Small and Effective Pattern Networks | cs.CV | While going deeper has been witnessed to improve the performance of
convolutional neural networks (CNN), going smaller for CNN has received
increasing attention recently due to its attractiveness for mobile/embedded
applications. It remains an active and important topic how to design a small
network while retaining the... | computer science |
8,592 | Teaching Compositionality to CNNs | cs.CV | Convolutional neural networks (CNNs) have shown great success in computer
vision, approaching human-level performance when trained for specific tasks via
application-specific loss functions. In this paper, we propose a method for
augmenting and training CNNs so that their learned features are compositional.
It encourag... | computer science |
8,593 | Effective Sequential Classifier Training for SVM-based Multitemporal
Remote Sensing Image Classification | cs.CV | The explosive availability of remote sensing images has challenged supervised
classification algorithms such as Support Vector Machines (SVM), as training
samples tend to be highly limited due to the expensive and laborious task of
ground truthing. The temporal correlation and spectral similarity between
multitemporal ... | computer science |
8,594 | Human-like Clustering with Deep Convolutional Neural Networks | cs.LG | Classification and clustering have been studied separately in machine
learning and computer vision. Inspired by the recent success of deep learning
models in solving various vision problems (e.g., object recognition, semantic
segmentation) and the fact that humans serve as the gold standard in assessing
clustering algo... | computer science |
8,595 | MEC: Memory-efficient Convolution for Deep Neural Network | cs.LG | Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or W... | computer science |
8,596 | Learning Efficient Point Cloud Generation for Dense 3D Object
Reconstruction | cs.CV | Conventional methods of 3D object generative modeling learn volumetric
predictions using deep networks with 3D convolutional operations, which are
direct analogies to classical 2D ones. However, these methods are
computationally wasteful in attempt to predict 3D shapes, where information is
rich only on the surfaces. I... | computer science |
8,597 | Balanced Quantization: An Effective and Efficient Approach to Quantized
Neural Networks | cs.CV | Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can b... | computer science |
8,598 | Pixels to Graphs by Associative Embedding | cs.CV | Graphs are a useful abstraction of image content. Not only can graphs
represent details about individual objects in a scene but they can capture the
interactions between pairs of objects. We present a method for training a
convolutional neural network such that it takes in an input image and produces
a full graph. This... | computer science |
8,599 | Multi-Label Learning with Label Enhancement | cs.LG | Multi-label learning deals with training instances associated with multiple
labels. Many common multi-label algorithms are to treat each label in a crisp
manner, being either relevant or irrelevant to an instance, and such label can
be called logical label. In contrast, we assume that there is a vector of
numerical lab... | computer science |
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