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1,200 | Compressing Convolutional Neural Networks | cs.LG | Convolutional neural networks (CNN) are increasingly used in many areas of
computer vision. They are particularly attractive because of their ability to
"absorb" great quantities of labeled data through millions of parameters.
However, as model sizes increase, so do the storage and memory requirements of
the classifier... | computer science |
1,201 | Understanding Neural Networks Through Deep Visualization | cs.CV | Recent years have produced great advances in training large, deep neural
networks (DNNs), including notable successes in training convolutional neural
networks (convnets) to recognize natural images. However, our understanding of
how these models work, especially what computations they perform at
intermediate layers, h... | computer science |
1,202 | Massively Deep Artificial Neural Networks for Handwritten Digit
Recognition | cs.CV | Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on
the famous MNIST database of handwritten digits. All that was required to
achieve this result was a high number of hidden layers consisting of many
neurons, and a graphics card to greatly speed up the rate of learning. | computer science |
1,203 | Compression of Fully-Connected Layer in Neural Network by Kronecker
Product | cs.NE | In this paper we propose and study a technique to reduce the number of
parameters and computation time in fully-connected layers of neural networks
using Kronecker product, at a mild cost of the prediction quality. The
technique proceeds by replacing Fully-Connected layers with so-called Kronecker
Fully-Connected layer... | computer science |
1,204 | Deep Learning for Single-View Instance Recognition | cs.CV | Deep learning methods have typically been trained on large datasets in which
many training examples are available. However, many real-world product datasets
have only a small number of images available for each product. We explore the
use of deep learning methods for recognizing object instances when we have only
a sin... | computer science |
1,205 | A Visual Embedding for the Unsupervised Extraction of Abstract Semantics | cs.CV | Vector-space word representations obtained from neural network models have
been shown to enable semantic operations based on vector arithmetic. In this
paper, we explore the existence of similar information on vector
representations of images. For that purpose we define a methodology to obtain
large, sparse vector repr... | computer science |
1,206 | On the Importance of Normalisation Layers in Deep Learning with
Piecewise Linear Activation Units | cs.CV | Deep feedforward neural networks with piecewise linear activations are
currently producing the state-of-the-art results in several public datasets.
The combination of deep learning models and piecewise linear activation
functions allows for the estimation of exponentially complex functions with the
use of a large numbe... | computer science |
1,207 | Do Deep Neural Networks Learn Facial Action Units When Doing Expression
Recognition? | cs.CV | Despite being the appearance-based classifier of choice in recent years,
relatively few works have examined how much convolutional neural networks
(CNNs) can improve performance on accepted expression recognition benchmarks
and, more importantly, examine what it is they actually learn. In this work,
not only do we show... | computer science |
1,208 | Stereo Matching by Training a Convolutional Neural Network to Compare
Image Patches | cs.CV | We present a method for extracting depth information from a rectified image
pair. Our approach focuses on the first stage of many stereo algorithms: the
matching cost computation. We approach the problem by learning a similarity
measure on small image patches using a convolutional neural network. Training
is carried ou... | computer science |
1,209 | ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional
Networks on Multi-Core and Many-Core Shared Memory Machines | cs.NE | Convolutional networks (ConvNets) have become a popular approach to computer
vision. It is important to accelerate ConvNet training, which is
computationally costly. We propose a novel parallel algorithm based on
decomposition into a set of tasks, most of which are convolutions or FFTs.
Applying Brent's theorem to the ... | computer science |
1,210 | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
Segmentation | cs.CV | We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder... | computer science |
1,211 | Visual7W: Grounded Question Answering in Images | cs.CV | We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's cap... | computer science |
1,212 | Deep learning is a good steganalysis tool when embedding key is reused
for different images, even if there is a cover source-mismatch | cs.MM | Since the BOSS competition, in 2010, most steganalysis approaches use a
learning methodology involving two steps: feature extraction, such as the Rich
Models (RM), for the image representation, and use of the Ensemble Classifier
(EC) for the learning step. In 2015, Qian et al. have shown that the use of a
deep learning... | computer science |
1,213 | Structural-RNN: Deep Learning on Spatio-Temporal Graphs | cs.CV | Deep Recurrent Neural Network architectures, though remarkably capable at
modeling sequences, lack an intuitive high-level spatio-temporal structure.
That is while many problems in computer vision inherently have an underlying
high-level structure and can benefit from it. Spatio-temporal graphs are a
popular tool for i... | computer science |
1,214 | Learning Neural Network Architectures using Backpropagation | cs.LG | Deep neural networks with millions of parameters are at the heart of many
state of the art machine learning models today. However, recent works have
shown that models with much smaller number of parameters can also perform just
as well. In this work, we introduce the problem of architecture-learning, i.e;
learning the ... | computer science |
1,215 | Variable Rate Image Compression with Recurrent Neural Networks | cs.CV | A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of th... | computer science |
1,216 | Why M Heads are Better than One: Training a Diverse Ensemble of Deep
Networks | cs.CV | Convolutional Neural Networks have achieved state-of-the-art performance on a
wide range of tasks. Most benchmarks are led by ensembles of these powerful
learners, but ensembling is typically treated as a post-hoc procedure
implemented by averaging independently trained models with model variation
induced by bagging or... | computer science |
1,217 | Training CNNs with Low-Rank Filters for Efficient Image Classification | cs.CV | We propose a new method for creating computationally efficient convolutional
neural networks (CNNs) by using low-rank representations of convolutional
filters. Rather than approximating filters in previously-trained networks with
more efficient versions, we learn a set of small basis filters from scratch;
during traini... | computer science |
1,218 | Gradual DropIn of Layers to Train Very Deep Neural Networks | cs.NE | We introduce the concept of dynamically growing a neural network during
training. In particular, an untrainable deep network starts as a trainable
shallow network and newly added layers are slowly, organically added during
training, thereby increasing the network's depth. This is accomplished by a new
layer, which we c... | computer science |
1,219 | LocNet: Improving Localization Accuracy for Object Detection | cs.CV | We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditi... | computer science |
1,220 | Towards Dropout Training for Convolutional Neural Networks | cs.LG | Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in convolutional and pooling layers is still not
clear. This paper demonstrates that max-pooling dropout is equivalent to
randomly picking acti... | computer science |
1,221 | Predicting and visualizing psychological attributions with a deep neural
network | cs.CV | Judgments about personality based on facial appearance are strong effectors
in social decision making, and are known to have impact on areas from
presidential elections to jury decisions. Recent work has shown that it is
possible to predict perception of memorability, trustworthiness, intelligence
and other attributes ... | computer science |
1,222 | Max-Pooling Dropout for Regularization of Convolutional Neural Networks | cs.LG | Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in pooling layers is still not clear. This paper
demonstrates that max-pooling dropout is equivalent to randomly picking
activation based on a ... | computer science |
1,223 | Creation of a Deep Convolutional Auto-Encoder in Caffe | cs.NE | The development of a deep (stacked) convolutional auto-encoder in the Caffe
deep learning framework is presented in this paper. We describe simple
principles which we used to create this model in Caffe. The proposed model of
convolutional auto-encoder does not have pooling/unpooling layers yet. The
results of our exper... | computer science |
1,224 | Affinity CNN: Learning Pixel-Centric Pairwise Relations for
Figure/Ground Embedding | cs.CV | Spectral embedding provides a framework for solving perceptual organization
problems, including image segmentation and figure/ground organization. From an
affinity matrix describing pairwise relationships between pixels, it clusters
pixels into regions, and, using a complex-valued extension, orders pixels
according to ... | computer science |
1,225 | On non-iterative training of a neural classifier | cs.CV | Recently an algorithm, was discovered, which separates points in n-dimension
by planes in such a manner that no two points are left un-separated by at least
one plane{[}1-3{]}. By using this new algorithm we show that there are two ways
of classification by a neural network, for a large dimension feature space,
both of... | computer science |
1,226 | GraphConnect: A Regularization Framework for Neural Networks | cs.CV | Deep neural networks have proved very successful in domains where large
training sets are available, but when the number of training samples is small,
their performance suffers from overfitting. Prior methods of reducing
overfitting such as weight decay, Dropout and DropConnect are data-independent.
This paper proposes... | computer science |
1,227 | Recent Advances in Convolutional Neural Networks | cs.CV | In the last few years, deep learning has led to very good performance on a
variety of problems, such as visual recognition, speech recognition and natural
language processing. Among different types of deep neural networks,
convolutional neural networks have been most extensively studied. Leveraging on
the rapid growth ... | computer science |
1,228 | Brain-Inspired Deep Networks for Image Aesthetics Assessment | cs.CV | Image aesthetics assessment has been challenging due to its subjective
nature. Inspired by the scientific advances in the human visual perception and
neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task.
BDN first learns attributes through the parallel supervised pathways, on a
variety of selecte... | computer science |
1,229 | Automatic Moth Detection from Trap Images for Pest Management | cs.CV | Monitoring the number of insect pests is a crucial component in
pheromone-based pest management systems. In this paper, we propose an automatic
detection pipeline based on deep learning for identifying and counting pests in
images taken inside field traps. Applied to a commercial codling moth dataset,
our method shows ... | computer science |
1,230 | Learning Typographic Style | cs.CV | Typography is a ubiquitous art form that affects our understanding,
perception, and trust in what we read. Thousands of different font-faces have
been created with enormous variations in the characters. In this paper, we
learn the style of a font by analyzing a small subset of only four letters.
From these four letters... | computer science |
1,231 | Early Detection of Combustion Instabilities using Deep Convolutional
Selective Autoencoders on Hi-speed Flame Video | cs.CV | This paper proposes an end-to-end convolutional selective autoencoder
approach for early detection of combustion instabilities using rapidly arriving
flame image frames. The instabilities arising in combustion processes cause
significant deterioration and safety issues in various human-engineered systems
such as land a... | computer science |
1,232 | Sparse Activity and Sparse Connectivity in Supervised Learning | cs.LG | Sparseness is a useful regularizer for learning in a wide range of
applications, in particular in neural networks. This paper proposes a model
targeted at classification tasks, where sparse activity and sparse connectivity
are used to enhance classification capabilities. The tool for achieving this is
a sparseness-enfo... | computer science |
1,233 | Deep Networks with Stochastic Depth | cs.LG | Very deep convolutional networks with hundreds of layers have led to
significant reductions in error on competitive benchmarks. Although the
unmatched expressiveness of the many layers can be highly desirable at test
time, training very deep networks comes with its own set of challenges. The
gradients can vanish, the f... | computer science |
1,234 | Accelerating Deep Learning with Shrinkage and Recall | cs.LG | Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorith... | computer science |
1,235 | Ristretto: Hardware-Oriented Approximation of Convolutional Neural
Networks | cs.CV | Convolutional neural networks (CNN) have achieved major breakthroughs in
recent years. Their performance in computer vision have matched and in some
areas even surpassed human capabilities. Deep neural networks can capture
complex non-linear features; however this ability comes at the cost of high
computational and mem... | computer science |
1,236 | SNN: Stacked Neural Networks | cs.LG | It has been proven that transfer learning provides an easy way to achieve
state-of-the-art accuracies on several vision tasks by training a simple
classifier on top of features obtained from pre-trained neural networks. The
goal of this work is to generate better features for transfer learning from
multiple publicly av... | computer science |
1,237 | Improved Techniques for Training GANs | cs.LG | We present a variety of new architectural features and training procedures
that we apply to the generative adversarial networks (GANs) framework. We focus
on two applications of GANs: semi-supervised learning, and the generation of
images that humans find visually realistic. Unlike most work on generative
models, our p... | computer science |
1,238 | A Powerful Generative Model Using Random Weights for the Deep Image
Representation | cs.CV | To what extent is the success of deep visualization due to the training?
Could we do deep visualization using untrained, random weight networks? To
address this issue, we explore new and powerful generative models for three
popular deep visualization tasks using untrained, random weight convolutional
neural networks. F... | computer science |
1,239 | Network Trimming: A Data-Driven Neuron Pruning Approach towards
Efficient Deep Architectures | cs.NE | State-of-the-art neural networks are getting deeper and wider. While their
performance increases with the increasing number of layers and neurons, it is
crucial to design an efficient deep architecture in order to reduce
computational and memory costs. Designing an efficient neural network, however,
is labor intensive ... | computer science |
1,240 | CNN-based Patch Matching for Optical Flow with Thresholded Hinge
Embedding Loss | cs.CV | Learning based approaches have not yet achieved their full potential in
optical flow estimation, where their performance still trails heuristic
approaches. In this paper, we present a CNN based patch matching approach for
optical flow estimation. An important contribution of our approach is a novel
thresholded loss for... | computer science |
1,241 | Attention Tree: Learning Hierarchies of Visual Features for Large-Scale
Image Recognition | cs.CV | One of the key challenges in machine learning is to design a computationally
efficient multi-class classifier while maintaining the output accuracy and
performance. In this paper, we present a tree-based classifier: Attention Tree
(ATree) for large-scale image classification that uses recursive Adaboost
training to con... | computer science |
1,242 | OnionNet: Sharing Features in Cascaded Deep Classifiers | cs.CV | The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages m... | computer science |
1,243 | Dynamic Network Surgery for Efficient DNNs | cs.NE | Deep learning has become a ubiquitous technology to improve machine
intelligence. However, most of the existing deep models are structurally very
complex, making them difficult to be deployed on the mobile platforms with
limited computational power. In this paper, we propose a novel network
compression method called dy... | computer science |
1,244 | A Deep Metric for Multimodal Registration | cs.CV | Multimodal registration is a challenging problem in medical imaging due the
high variability of tissue appearance under different imaging modalities. The
crucial component here is the choice of the right similarity measure. We make a
step towards a general learning-based solution that can be adapted to specific
situati... | computer science |
1,245 | Memory-augmented Attention Modelling for Videos | cs.CV | We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already... | computer science |
1,246 | DelugeNets: Deep Networks with Efficient and Flexible Cross-layer
Information Inflows | cs.CV | Deluge Networks (DelugeNets) are deep neural networks which efficiently
facilitate massive cross-layer information inflows from preceding layers to
succeeding layers. The connections between layers in DelugeNets are established
through cross-layer depthwise convolutional layers with learnable filters,
acting as a flexi... | computer science |
1,247 | Learning the Number of Neurons in Deep Networks | cs.CV | Nowadays, the number of layers and of neurons in each layer of a deep network
are typically set manually. While very deep and wide networks have proven
effective in general, they come at a high memory and computation cost, thus
making them impractical for constrained platforms. These networks, however, are
known to hav... | computer science |
1,248 | Multigrid Neural Architectures | cs.CV | We propose a multigrid extension of convolutional neural networks (CNNs).
Rather than manipulating representations living on a single spatial grid, our
network layers operate across scale space, on a pyramid of grids. They consume
multigrid inputs and produce multigrid outputs; convolutional filters
themselves have bot... | computer science |
1,249 | Demystifying Neural Style Transfer | cs.CV | Neural Style Transfer has recently demonstrated very exciting results which
catches eyes in both academia and industry. Despite the amazing results, the
principle of neural style transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural ... | computer science |
1,250 | A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in
Caffe | cs.NE | This paper presents the development of several models of a deep convolutional
auto-encoder in the Caffe deep learning framework and their experimental
evaluation on the example of MNIST dataset. We have created five models of a
convolutional auto-encoder which differ architecturally by the presence or
absence of poolin... | computer science |
1,251 | Optimization on Product Submanifolds of Convolution Kernels | cs.CV | Recent advances in optimization methods used for training convolutional
neural networks (CNNs) with kernels, which are normalized according to
particular constraints, have shown remarkable success. This work introduces an
approach for training CNNs using ensembles of joint spaces of kernels
constructed using different ... | computer science |
1,252 | All You Need is Beyond a Good Init: Exploring Better Solution for
Training Extremely Deep Convolutional Neural Networks with Orthonormality and
Modulation | cs.CV | Deep neural network is difficult to train and this predicament becomes worse
as the depth increases. The essence of this problem exists in the magnitude of
backpropagated errors that will result in gradient vanishing or exploding
phenomenon. We show that a variant of regularizer which utilizes orthonormality
among diff... | computer science |
1,253 | Tree Memory Networks for Modelling Long-term Temporal Dependencies | cs.LG | In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully tra... | computer science |
1,254 | Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark | cs.CV | This paper presents a new 3D point cloud classification benchmark data set
with over four billion manually labelled points, meant as input for data-hungry
(deep) learning methods. We also discuss first submissions to the benchmark
that use deep convolutional neural networks (CNNs) as a work horse, which
already show re... | computer science |
1,255 | Introspective Generative Modeling: Decide Discriminatively | cs.CV | We study unsupervised learning by developing introspective generative
modeling (IGM) that attains a generator using progressively learned deep
convolutional neural networks. The generator is itself a discriminator, capable
of introspection: being able to self-evaluate the difference between its
generated samples and th... | computer science |
1,256 | Espresso: Efficient Forward Propagation for BCNNs | cs.DC | There are many applications scenarios for which the computational performance
and memory footprint of the prediction phase of Deep Neural Networks (DNNs)
needs to be optimized. Binary Neural Networks (BDNNs) have been shown to be an
effective way of achieving this objective. In this paper, we show how
Convolutional Neu... | computer science |
1,257 | BMXNet: An Open-Source Binary Neural Network Implementation Based on
MXNet | cs.LG | Binary Neural Networks (BNNs) can drastically reduce memory size and accesses
by applying bit-wise operations instead of standard arithmetic operations.
Therefore it could significantly improve the efficiency and lower the energy
consumption at runtime, which enables the application of state-of-the-art deep
learning mo... | computer science |
1,258 | Learning Local Receptive Fields and their Weight Sharing Scheme on
Graphs | cs.LG | We propose a simple and generic layer formulation that extends the properties
of convolutional layers to any domain that can be described by a graph. Namely,
we use the support of its adjacency matrix to design learnable weight sharing
filters able to exploit the underlying structure of signals in the same fashion
as f... | computer science |
1,259 | DarkRank: Accelerating Deep Metric Learning via Cross Sample
Similarities Transfer | cs.CV | We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also brin... | computer science |
1,260 | BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks | cs.NE | Deep neural networks are state of the art methods for many learning tasks due
to their ability to extract increasingly better features at each network layer.
However, the improved performance of additional layers in a deep network comes
at the cost of added latency and energy usage in feedforward inference. As
networks... | computer science |
1,261 | Deep Convolutional Neural Networks as Generic Feature Extractors | cs.CV | Recognizing objects in natural images is an intricate problem involving
multiple conflicting objectives. Deep convolutional neural networks, trained on
large datasets, achieve convincing results and are currently the
state-of-the-art approach for this task. However, the long time needed to train
such deep networks is a... | computer science |
1,262 | Generic 3D Representation via Pose Estimation and Matching | cs.CV | Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose ... | computer science |
1,263 | Adversarial Deep Structured Nets for Mass Segmentation from Mammograms | cs.CV | Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Be... | computer science |
1,264 | Deep Learning as a Mixed Convex-Combinatorial Optimization Problem | cs.LG | As neural networks grow deeper and wider, learning networks with
hard-threshold activations is becoming increasingly important, both for network
quantization, which can drastically reduce time and energy requirements, and
for creating large integrated systems of deep networks, which may have
non-differentiable componen... | computer science |
1,265 | Routing Networks: Adaptive Selection of Non-linear Functions for
Multi-Task Learning | cs.LG | Multi-task learning (MTL) with neural networks leverages commonalities in
tasks to improve performance, but often suffers from task interference which
reduces the benefits of transfer. To address this issue we introduce the
routing network paradigm, a novel neural network and training algorithm. A
routing network is a ... | computer science |
1,266 | Apprentice: Using Knowledge Distillation Techniques To Improve
Low-Precision Network Accuracy | cs.LG | Deep learning networks have achieved state-of-the-art accuracies on computer
vision workloads like image classification and object detection. The performant
systems, however, typically involve big models with numerous parameters. Once
trained, a challenging aspect for such top performing models is deployment on
resourc... | computer science |
1,267 | "Zero-Shot" Super-Resolution using Deep Internal Learning | cs.CV | Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance
in the past few years. However, being supervised, these SR methods are
restricted to specific training data, where the acquisition of the
low-resolution (LR) images from their high-resolution (HR) counterparts is
predetermined (e.g., bicubic ... | computer science |
1,268 | Dynamic Weight Alignment for Convolutional Neural Networks | cs.CV | In this paper, we propose a method of improving Convolutional Neural Networks
(CNN) by determining the optimal alignment of weights and inputs using dynamic
programming. Conventional CNNs convolve learnable shared weights, or filters,
across the input data. The filters use a linear matching of weights to inputs
using a... | computer science |
1,269 | Towards automated patient data cleaning using deep learning: A
feasibility study on the standardization of organ labeling | cs.CV | Data cleaning consumes about 80% of the time spent on data analysis for
clinical research projects. This is a much bigger problem in the era of big
data and machine learning in the field of medicine where large volumes of data
are being generated. We report an initial effort towards automated patient data
cleaning usin... | computer science |
1,270 | StressedNets: Efficient Feature Representations via Stress-induced
Evolutionary Synthesis of Deep Neural Networks | cs.CV | The computational complexity of leveraging deep neural networks for
extracting deep feature representations is a significant barrier to its
widespread adoption, particularly for use in embedded devices. One particularly
promising strategy to addressing the complexity issue is the notion of
evolutionary synthesis of dee... | computer science |
1,271 | DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule
Detection and Classification | cs.CV | In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lun... | computer science |
1,272 | Automatic construction of Chinese herbal prescription from tongue image
via CNNs and auxiliary latent therapy topics | cs.CV | The tongue image is an important physical information of human, it is of
great importance to the diagnosis and treatment in clinical medicine. Herbal
prescriptions are simple, noninvasive and low side effects, and are widely
applied in China. Researches on automatic construction technology of herbal
prescription based ... | computer science |
1,273 | Universal Deep Neural Network Compression | cs.CV | Compression of deep neural networks (DNNs) for memory- and
computation-efficient compact feature representations becomes a critical
problem particularly for deployment of DNNs on resource-limited platforms. In
this paper, we investigate lossy compression of DNNs by weight quantization and
lossless source coding for mem... | computer science |
1,274 | Plummer Autoencoders | cs.LG | Estimating the true density in high-dimensional feature spaces is a
well-known problem in machine learning. This work shows that it is possible to
formulate the optimization problem as a minimization and use the
representational power of neural networks to learn very complex densities. A
theoretical bound on the estima... | computer science |
1,275 | Analyzing and Mitigating the Impact of Permanent Faults on a Systolic
Array Based Neural Network Accelerator | cs.LG | Due to their growing popularity and computational cost, deep neural networks
(DNNs) are being targeted for hardware acceleration. A popular architecture for
DNN acceleration, adopted by the Google Tensor Processing Unit (TPU), utilizes
a systolic array based matrix multiplication unit at its core. This paper deals
with... | computer science |
1,276 | Efficient Sparse-Winograd Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) are computationally intensive, which
limits their application on mobile devices. Their energy is dominated by the
number of multiplies needed to perform the convolutions. Winograd's minimal
filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can
reduce the opera... | computer science |
1,277 | On Lyapunov exponents and adversarial perturbation | cs.CV | In this paper, we would like to disseminate a serendipitous discovery
involving Lyapunov exponents of a 1-D time series and their use in serving as a
filtering defense tool against a specific kind of deep adversarial
perturbation. To this end, we use the state-of-the-art CleverHans library to
generate adversarial pertu... | computer science |
1,278 | Demystifying Parallel and Distributed Deep Learning: An In-Depth
Concurrency Analysis | cs.LG | Deep Neural Networks (DNNs) are becoming an important tool in modern
computing applications. Accelerating their training is a major challenge and
techniques range from distributed algorithms to low-level circuit design. In
this survey, we describe the problem from a theoretical perspective, followed
by approaches for i... | computer science |
1,279 | Robust Combining of Disparate Classifiers through Order Statistics | cs.LG | Integrating the outputs of multiple classifiers via combiners or
meta-learners has led to substantial improvements in several difficult pattern
recognition problems. In the typical setting investigated till now, each
classifier is trained on data taken or resampled from a common data set, or
(almost) randomly selected ... | computer science |
1,280 | Deep Self-Taught Learning for Handwritten Character Recognition | cs.LG | Recent theoretical and empirical work in statistical machine learning has
demonstrated the importance of learning algorithms for deep architectures,
i.e., function classes obtained by composing multiple non-linear
transformations. Self-taught learning (exploiting unlabeled examples or
examples from other distributions)... | computer science |
1,281 | A New 2.5D Representation for Lymph Node Detection using Random Sets of
Deep Convolutional Neural Network Observations | cs.CV | Automated Lymph Node (LN) detection is an important clinical diagnostic task
but very challenging due to the low contrast of surrounding structures in
Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely
distributed locations. State-of-the-art studies show the performance range of
52.9% sensi... | computer science |
1,282 | Heterogeneous Multi-task Learning for Human Pose Estimation with Deep
Convolutional Neural Network | cs.CV | We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part dete... | computer science |
1,283 | Discriminative Unsupervised Feature Learning with Exemplar Convolutional
Neural Networks | cs.LG | Deep convolutional networks have proven to be very successful in learning
task specific features that allow for unprecedented performance on various
computer vision tasks. Training of such networks follows mostly the supervised
learning paradigm, where sufficiently many input-output pairs are required for
training. Acq... | computer science |
1,284 | Improving neural networks by preventing co-adaptation of feature
detectors | cs.NE | When a large feedforward neural network is trained on a small training set,
it typically performs poorly on held-out test data. This "overfitting" is
greatly reduced by randomly omitting half of the feature detectors on each
training case. This prevents complex co-adaptations in which a feature detector
is only helpful... | computer science |
1,285 | A New Training Algorithm for Kanerva's Sparse Distributed Memory | cs.CV | The Sparse Distributed Memory proposed by Pentii Kanerva (SDM in short) was
thought to be a model of human long term memory. The architecture of the SDM
permits to store binary patterns and to retrieve them using partially matching
patterns. However Kanerva's model is especially efficient only in handling
random data. ... | computer science |
1,286 | Accelerating Very Deep Convolutional Networks for Classification and
Detection | cs.CV | This paper aims to accelerate the test-time computation of convolutional
neural networks (CNNs), especially very deep CNNs that have substantially
impacted the computer vision community. Unlike previous methods that are
designed for approximating linear filters or linear responses, our method takes
the nonlinear units ... | computer science |
1,287 | Boosting-like Deep Learning For Pedestrian Detection | cs.CV | This paper proposes boosting-like deep learning (BDL) framework for
pedestrian detection. Due to overtraining on the limited training samples,
overfitting is a major problem of deep learning. We incorporate a boosting-like
technique into deep learning to weigh the training samples, and thus prevent
overtraining in the ... | computer science |
1,288 | Finding Optimal Combination of Kernels using Genetic Programming | cs.CV | In Computer Vision, problem of identifying or classifying the objects present
in an image is called Object Categorization. It is a challenging problem,
especially when the images have clutter background, occlusions or different
lighting conditions. Many vision features have been proposed which aid object
categorization... | computer science |
1,289 | Visualization Regularizers for Neural Network based Image Recognition | cs.LG | The success of deep neural networks is mostly due their ability to learn
meaningful features from the data. Features learned in the hidden layers of
deep neural networks trained in computer vision tasks have been shown to be
similar to mid-level vision features. We leverage this fact in this work and
propose the visual... | computer science |
1,290 | Reservoir computing for spatiotemporal signal classification without
trained output weights | cs.NE | Reservoir computing is a recently introduced machine learning paradigm that
has been shown to be well-suited for the processing of spatiotemporal data.
Rather than training the network node connections and weights via
backpropagation in traditional recurrent neural networks, reservoirs instead
have fixed connections an... | computer science |
1,291 | Self-taught learning of a deep invariant representation for visual
tracking via temporal slowness principle | cs.CV | Visual representation is crucial for a visual tracking method's performances.
Conventionally, visual representations adopted in visual tracking rely on
hand-crafted computer vision descriptors. These descriptors were developed
generically without considering tracking-specific information. In this paper,
we propose to l... | computer science |
1,292 | CNN-RNN: A Unified Framework for Multi-label Image Classification | cs.CV | While deep convolutional neural networks (CNNs) have shown a great success in
single-label image classification, it is important to note that real world
images generally contain multiple labels, which could correspond to different
objects, scenes, actions and attributes in an image. Traditional approaches to
multi-labe... | computer science |
1,293 | Efficient Dictionary Learning with Sparseness-Enforcing Projections | cs.LG | Learning dictionaries suitable for sparse coding instead of using engineered
bases has proven effective in a variety of image processing tasks. This paper
studies the optimization of dictionaries on image data where the representation
is enforced to be explicitly sparse with respect to a smooth, normalized
sparseness m... | computer science |
1,294 | End to End Learning for Self-Driving Cars | cs.CV | We trained a convolutional neural network (CNN) to map raw pixels from a
single front-facing camera directly to steering commands. This end-to-end
approach proved surprisingly powerful. With minimum training data from humans
the system learns to drive in traffic on local roads with or without lane
markings and on highw... | computer science |
1,295 | Deeply Coupled Auto-encoder Networks for Cross-view Classification | cs.CV | The comparison of heterogeneous samples extensively exists in many
applications, especially in the task of image classification. In this paper, we
propose a simple but effective coupled neural network, called Deeply Coupled
Autoencoder Networks (DCAN), which seeks to build two deep neural networks,
coupled with each ot... | computer science |
1,296 | An enhanced neural network based approach towards object extraction | cs.CV | The improvements in spectral and spatial resolution of the satellite images
have facilitated the automatic extraction and identification of the features
from satellite images and aerial photographs. An automatic object extraction
method is presented for extracting and identifying the various objects from
satellite imag... | computer science |
1,297 | A brief survey on deep belief networks and introducing a new object
oriented toolbox (DeeBNet) | cs.CV | Nowadays, this is very popular to use the deep architectures in machine
learning. Deep Belief Networks (DBNs) are deep architectures that use stack of
Restricted Boltzmann Machines (RBM) to create a powerful generative model using
training data. DBNs have many ability like feature extraction and
classification that are... | computer science |
1,298 | Real-time emotion recognition for gaming using deep convolutional
network features | cs.CV | The goal of the present study is to explore the application of deep
convolutional network features to emotion recognition. Results indicate that
they perform similarly to other published models at a best recognition rate of
94.4%, and do so with a single still image rather than a video stream. An
implementation of an a... | computer science |
1,299 | Caffe: Convolutional Architecture for Fast Feature Embedding | cs.CV | Caffe provides multimedia scientists and practitioners with a clean and
modifiable framework for state-of-the-art deep learning algorithms and a
collection of reference models. The framework is a BSD-licensed C++ library
with Python and MATLAB bindings for training and deploying general-purpose
convolutional neural net... | computer science |
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