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1,100 | Stochastic Gradient Descent: Going As Fast As Possible But Not Faster | stat.ML | When applied to training deep neural networks, stochastic gradient descent
(SGD) often incurs steady progression phases, interrupted by catastrophic
episodes in which loss and gradient norm explode. A possible mitigation of such
events is to slow down the learning process. This paper presents a novel
approach to contro... | computer science |
1,101 | On the exact relationship between the denoising function and the data
distribution | cs.NE | We prove an exact relationship between the optimal denoising function and the
data distribution in the case of additive Gaussian noise, showing that
denoising implicitly models the structure of data allowing it to be exploited
in the unsupervised learning of representations. This result generalizes a
known relationship... | computer science |
1,102 | Shifting Mean Activation Towards Zero with Bipolar Activation Functions | stat.ML | We propose a simple extension to the ReLU-family of activation functions that
allows them to shift the mean activation across a layer towards zero. Combined
with proper weight initialization, this alleviates the need for normalization
layers. We explore the training of deep vanilla recurrent neural networks
(RNNs) with... | computer science |
1,103 | Minimal Effort Back Propagation for Convolutional Neural Networks | cs.LG | As traditional neural network consumes a significant amount of computing
resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet
effective technique to alleviate this problem. In this technique, only a small
subset of the full gradients are computed to update the model parameters. In
this paper ... | computer science |
1,104 | EDEN: Evolutionary Deep Networks for Efficient Machine Learning | stat.ML | Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Net... | computer science |
1,105 | Deep Learning for Unsupervised Insider Threat Detection in Structured
Cybersecurity Data Streams | cs.NE | Analysis of an organization's computer network activity is a key component of
early detection and mitigation of insider threat, a growing concern for many
organizations. Raw system logs are a prototypical example of streaming data
that can quickly scale beyond the cognitive power of a human analyst. As a
prospective fi... | computer science |
1,106 | Training Feedforward Neural Networks with Standard Logistic Activations
is Feasible | cs.NE | Training feedforward neural networks with standard logistic activations is
considered difficult because of the intrinsic properties of these sigmoidal
functions. This work aims at showing that these networks can be trained to
achieve generalization performance comparable to those based on hyperbolic
tangent activations... | computer science |
1,107 | full-FORCE: A Target-Based Method for Training Recurrent Networks | cs.NE | Trained recurrent networks are powerful tools for modeling dynamic neural
computations. We present a target-based method for modifying the full
connectivity matrix of a recurrent network to train it to perform tasks
involving temporally complex input/output transformations. The method
introduces a second network during... | computer science |
1,108 | StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks | cs.LG | A widely studied non-deterministic polynomial time (NP) hard problem lies in
finding a route between the two nodes of a graph. Often meta-heuristics
algorithms such as $A^{*}$ are employed on graphs with a large number of nodes.
Here, we propose a deep recurrent neural network architecture based on the
Sequence-2-Seque... | computer science |
1,109 | Sum-Product-Quotient Networks | cs.LG | We present a novel tractable generative model that extends Sum-Product
Networks (SPNs) and significantly boosts their power. We call it
Sum-Product-Quotient Networks (SPQNs), whose core concept is to incorporate
conditional distributions into the model by direct computation using quotient
nodes, e.g. $P(A|B) = \frac{P(... | computer science |
1,110 | Learning compressed representations of blood samples time series with
missing data | cs.NE | Clinical measurements collected over time are naturally represented as
multivariate time series (MTS), which often contain missing data. An
autoencoder can learn low dimensional vectorial representations of MTS that
preserve important data characteristics, but cannot deal explicitly with
missing data. In this work, we ... | computer science |
1,111 | Biologically Inspired Feedforward Supervised Learning for Deep
Self-Organizing Map Networks | stat.ML | In this study, we propose a novel deep neural network and its supervised
learning method that uses a feedforward supervisory signal. The method is
inspired by the human visual system and performs human-like association-based
learning without any backward error propagation. The feedforward supervisory
signal that produc... | computer science |
1,112 | PDE-Net: Learning PDEs from Data | math.NA | In this paper, we present an initial attempt to learn evolution PDEs from
data. Inspired by the latest development of neural network designs in deep
learning, we propose a new feed-forward deep network, called PDE-Net, to
fulfill two objectives at the same time: to accurately predict dynamics of
complex systems and to ... | computer science |
1,113 | On the role of synaptic stochasticity in training low-precision neural
networks | cs.LG | Stochasticity and limited precision of synaptic weights in neural network
models are key aspects of both biological and hardware modeling of learning
processes. Here we show that a neural network model with stochastic binary
weights naturally gives prominence to exponentially rare dense regions of
solutions with a numb... | computer science |
1,114 | Smooth Neighbors on Teacher Graphs for Semi-supervised Learning | cs.LG | The paper proposes an inductive semi-supervised learning method, called
Smooth Neighbors on Teacher Graphs (SNTG). At each iteration during training, a
graph is dynamically constructed based on predictions of the teacher model,
i.e., the implicit self-ensemble of models. Then the graph serves as a
similarity measure wi... | computer science |
1,115 | Machine Learning Approach to RF Transmitter Identification | eess.SP | With the development and widespread use of wireless devices in recent years
(mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has
become extremely crowded. In order to counter security threats posed by rogue
or unknown transmitters, it is important to identify RF transmitters not by the
data cont... | computer science |
1,116 | Variational Walkback: Learning a Transition Operator as a Stochastic
Recurrent Net | stat.ML | We propose a novel method to directly learn a stochastic transition operator
whose repeated application provides generated samples. Traditional undirected
graphical models approach this problem indirectly by learning a Markov chain
model whose stationary distribution obeys detailed balance with respect to a
parameteriz... | computer science |
1,117 | Lower bounds over Boolean inputs for deep neural networks with ReLU
gates | cs.CC | Motivated by the resurgence of neural networks in being able to solve complex
learning tasks we undertake a study of high depth networks using ReLU gates
which implement the function $x \mapsto \max\{0,x\}$. We try to understand the
role of depth in such neural networks by showing size lowerbounds against such
network ... | computer science |
1,118 | Latent Constraints: Learning to Generate Conditionally from
Unconditional Generative Models | cs.LG | Deep generative neural networks have proven effective at both conditional and
unconditional modeling of complex data distributions. Conditional generation
enables interactive control, but creating new controls often requires expensive
retraining. In this paper, we develop a method to condition generation without
retrai... | computer science |
1,119 | Deep supervised learning using local errors | cs.NE | Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile... | computer science |
1,120 | Simulating Human Grandmasters: Evolution and Coevolution of Evaluation
Functions | cs.NE | This paper demonstrates the use of genetic algorithms for evolving a
grandmaster-level evaluation function for a chess program. This is achieved by
combining supervised and unsupervised learning. In the supervised learning
phase the organisms are evolved to mimic the behavior of human grandmasters,
and in the unsupervi... | computer science |
1,121 | Expert-Driven Genetic Algorithms for Simulating Evaluation Functions | cs.NE | In this paper we demonstrate how genetic algorithms can be used to reverse
engineer an evaluation function's parameters for computer chess. Our results
show that using an appropriate expert (or mentor), we can evolve a program that
is on par with top tournament-playing chess programs, outperforming a two-time
World Com... | computer science |
1,122 | Genetic Algorithms for Evolving Deep Neural Networks | cs.NE | In recent years, deep learning methods applying unsupervised learning to
train deep layers of neural networks have achieved remarkable results in
numerous fields. In the past, many genetic algorithms based methods have been
successfully applied to training neural networks. In this paper, we extend
previous work and pro... | computer science |
1,123 | Variational Probability Flow for Biologically Plausible Training of Deep
Neural Networks | cs.LG | The quest for biologically plausible deep learning is driven, not just by the
desire to explain experimentally-observed properties of biological neural
networks, but also by the hope of discovering more efficient methods for
training artificial networks. In this paper, we propose a new algorithm named
Variational Proba... | computer science |
1,124 | DeepSign: Deep Learning for Automatic Malware Signature Generation and
Classification | cs.CR | This paper presents a novel deep learning based method for automatic malware
signature generation and classification. The method uses a deep belief network
(DBN), implemented with a deep stack of denoising autoencoders, generating an
invariant compact representation of the malware behavior. While conventional
signature... | computer science |
1,125 | Genetic Algorithms for Evolving Computer Chess Programs | cs.NE | This paper demonstrates the use of genetic algorithms for evolving: 1) a
grandmaster-level evaluation function, and 2) a search mechanism for a chess
program, the parameter values of which are initialized randomly. The evaluation
function of the program is evolved by learning from databases of (human)
grandmaster games... | computer science |
1,126 | DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural
Networks | cs.CR | In recent years numerous advanced malware, aka advanced persistent threats
(APT) are allegedly developed by nation-states. The task of attributing an APT
to a specific nation-state is extremely challenging for several reasons. Each
nation-state has usually more than a single cyber unit that develops such
advanced malwa... | computer science |
1,127 | DeepChess: End-to-End Deep Neural Network for Automatic Learning in
Chess | cs.NE | We present an end-to-end learning method for chess, relying on deep neural
networks. Without any a priori knowledge, in particular without any knowledge
regarding the rules of chess, a deep neural network is trained using a
combination of unsupervised pretraining and supervised training. The
unsupervised training extra... | computer science |
1,128 | Gaussian Process Neurons Learn Stochastic Activation Functions | stat.ML | We propose stochastic, non-parametric activation functions that are fully
learnable and individual to each neuron. Complexity and the risk of overfitting
are controlled by placing a Gaussian process prior over these functions. The
result is the Gaussian process neuron, a probabilistic unit that can be used as
the basic... | computer science |
1,129 | Single-trial P300 Classification using PCA with LDA, QDA and Neural
Networks | cs.NE | The P300 event-related potential (ERP), evoked in scalp-recorded
electroencephalography (EEG) by external stimuli, has proven to be a reliable
response for controlling a BCI. The P300 component of an event related
potential is thus widely used in brain-computer interfaces to translate the
subjects' intent by mere thoug... | computer science |
1,130 | Neural Component Analysis for Fault Detection | cs.LG | Principal component analysis (PCA) is largely adopted for chemical process
monitoring and numerous PCA-based systems have been developed to solve various
fault detection and diagnosis problems. Since PCA-based methods assume that the
monitored process is linear, nonlinear PCA models, such as autoencoder models
and kern... | computer science |
1,131 | Concept Formation and Dynamics of Repeated Inference in Deep Generative
Models | stat.ML | Deep generative models are reported to be useful in broad applications
including image generation. Repeated inference between data space and latent
space in these models can denoise cluttered images and improve the quality of
inferred results. However, previous studies only qualitatively evaluated image
outputs in data... | computer science |
1,132 | Size-Independent Sample Complexity of Neural Networks | cs.LG | We study the sample complexity of learning neural networks, by providing new
bounds on their Rademacher complexity assuming norm constraints on the
parameter matrix of each layer. Compared to previous work, these complexity
bounds have improved dependence on the network depth, and under some additional
assumptions, are... | computer science |
1,133 | Rapid Adaptation with Conditionally Shifted Neurons | cs.LG | We describe a mechanism by which artificial neural networks can learn rapid
adaptation - the ability to adapt on the fly, with little data, to new tasks -
that we call conditionally shifted neurons. We apply this mechanism in the
framework of metalearning, where the aim is to replicate some of the
flexibility of human ... | computer science |
1,134 | Flexible Deep Neural Network Processing | cs.NE | The recent success of Deep Neural Networks (DNNs) has drastically improved
the state of the art for many application domains. While achieving high
accuracy performance, deploying state-of-the-art DNNs is a challenge since they
typically require billions of expensive arithmetic computations. In addition,
DNNs are typica... | computer science |
1,135 | ReNN: Rule-embedded Neural Networks | cs.LG | The artificial neural network shows powerful ability of inference, but it is
still criticized for lack of interpretability and prerequisite needs of big
dataset. This paper proposes the Rule-embedded Neural Network (ReNN) to
overcome the shortages. ReNN first makes local-based inferences to detect local
patterns, and t... | computer science |
1,136 | Imitation networks: Few-shot learning of neural networks from scratch | stat.ML | In this paper, we propose imitation networks, a simple but effective method
for training neural networks with a limited amount of training data. Our
approach inherits the idea of knowledge distillation that transfers knowledge
from a deep or wide reference model to a shallow or narrow target model. The
proposed method ... | computer science |
1,137 | Junction Tree Variational Autoencoder for Molecular Graph Generation | cs.LG | We seek to automate the design of molecules based on specific chemical
properties. In computational terms, this task involves continuous embedding and
generation of molecular graphs. Our primary contribution is the direct
realization of molecular graphs, a task previously approached by generating
linear SMILES strings ... | computer science |
1,138 | Anomaly Detection using One-Class Neural Networks | cs.LG | We propose a one-class neural network (OC-NN) model to detect anomalies in
complex data sets. OC-NN combines the ability of deep networks to extract
progressively rich representation of data with the one-class objective of
creating a tight envelope around normal data. The OC-NN approach breaks new
ground for the follow... | computer science |
1,139 | Adversarial Training for Probabilistic Spiking Neural Networks | stat.ML | Classifiers trained using conventional empirical risk minimization or maximum
likelihood methods are known to suffer dramatic performance degradations when
tested over examples adversarially selected based on knowledge of the
classifier's decision rule. Due to the prominence of Artificial Neural Networks
(ANNs) as clas... | computer science |
1,140 | Improving Graph Convolutional Networks with Non-Parametric Activation
Functions | cs.NE | Graph neural networks (GNNs) are a class of neural networks that allow to
efficiently perform inference on data that is associated to a graph structure,
such as, e.g., citation networks or knowledge graphs. While several variants of
GNNs have been proposed, they only consider simple nonlinear activation
functions in th... | computer science |
1,141 | Evolutionary Generative Adversarial Networks | cs.LG | Generative adversarial networks (GAN) have been effective for learning
generative models for real-world data. However, existing GANs (GAN and its
variants) tend to suffer from training problems such as instability and mode
collapse. In this paper, we propose a novel GAN framework called evolutionary
generative adversar... | computer science |
1,142 | Autostacker: A Compositional Evolutionary Learning System | cs.LG | We introduce an automatic machine learning (AutoML) modeling architecture
called Autostacker, which combines an innovative hierarchical stacking
architecture and an Evolutionary Algorithm (EA) to perform efficient parameter
search. Neither prior domain knowledge about the data nor feature preprocessing
is needed. Using... | computer science |
1,143 | Online Deep Learning: Growing RBM on the fly | cs.NE | We propose a novel online learning algorithm for Restricted Boltzmann
Machines (RBM), namely, the Online Generative Discriminative Restricted
Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the
network architecture of RBM according to the statistics of streaming data. The
OGD-RBM is trained in... | computer science |
1,144 | FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees | cs.LG | Recent DNN pruning algorithms have succeeded in reducing the number of
parameters in fully connected layers, often with little or no drop in
classification accuracy. However, most of the existing pruning schemes either
have to be applied during training or require a costly retraining procedure
after pruning to regain c... | computer science |
1,145 | Recurrent Neural Network Attention Mechanisms for Interpretable System
Log Anomaly Detection | cs.LG | Deep learning has recently demonstrated state-of-the art performance on key
tasks related to the maintenance of computer systems, such as intrusion
detection, denial of service attack detection, hardware and software system
failures, and malware detection. In these contexts, model interpretability is
vital for administ... | computer science |
1,146 | Deep architectures for learning context-dependent ranking functions | stat.ML | Object ranking is an important problem in the realm of preference learning.
On the basis of training data in the form of a set of rankings of objects,
which are typically represented as feature vectors, the goal is to learn a
ranking function that predicts a linear order of any new set of objects.
Current approaches co... | computer science |
1,147 | Convolutional Neural Networks Applied to House Numbers Digit
Classification | cs.CV | We classify digits of real-world house numbers using convolutional neural
networks (ConvNets). ConvNets are hierarchical feature learning neural networks
whose structure is biologically inspired. Unlike many popular vision approaches
that are hand-designed, ConvNets can automatically learn a unique set of
features opti... | computer science |
1,148 | The Neural Representation Benchmark and its Evaluation on Brain and
Machine | cs.NE | A key requirement for the development of effective learning representations
is their evaluation and comparison to representations we know to be effective.
In natural sensory domains, the community has viewed the brain as a source of
inspiration and as an implicit benchmark for success. However, it has not been
possible... | computer science |
1,149 | Training Convolutional Networks with Noisy Labels | cs.CV | The availability of large labeled datasets has allowed Convolutional Network
models to achieve impressive recognition results. However, in many settings
manual annotation of the data is impractical; instead our data has noisy
labels, i.e. there is some freely available label for each image which may or
may not be accur... | computer science |
1,150 | Object detection via a multi-region & semantic segmentation-aware CNN
model | cs.CV | We propose an object detection system that relies on a multi-region deep
convolutional neural network (CNN) that also encodes semantic
segmentation-aware features. The resulting CNN-based representation aims at
capturing a diverse set of discriminative appearance factors and exhibits
localization sensitivity that is es... | computer science |
1,151 | A PCA-Based Convolutional Network | cs.LG | In this paper, we propose a novel unsupervised deep learning model, called
PCA-based Convolutional Network (PCN). The architecture of PCN is composed of
several feature extraction stages and a nonlinear output stage. Particularly,
each feature extraction stage includes two layers: a convolutional layer and a
feature po... | computer science |
1,152 | An Empirical Evaluation of Current Convolutional Architectures' Ability
to Manage Nuisance Location and Scale Variability | cs.CV | We conduct an empirical study to test the ability of Convolutional Neural
Networks (CNNs) to reduce the effects of nuisance transformations of the input
data, such as location, scale and aspect ratio. We isolate factors by adopting
a common convolutional architecture either deployed globally on the image to
compute cla... | computer science |
1,153 | Binarized Neural Networks on the ImageNet Classification Task | cs.CV | We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet
ILSVRC-2102 dataset classification task and achieved a good performance. With a
moderate size network of 13 layers, we obtained top-5 classification accuracy
rate of 84.1 % on validation set through network distillation, much better than
previo... | computer science |
1,154 | Filling in the details: Perceiving from low fidelity images | cs.CV | Humans perceive their surroundings in great detail even though most of our
visual field is reduced to low-fidelity color-deprived (e.g. dichromatic) input
by the retina. In contrast, most deep learning architectures are
computationally wasteful in that they consider every part of the input when
performing an image proc... | computer science |
1,155 | Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks | cs.CV | Kinect skeleton tracker is able to achieve considerable human body tracking
performance in convenient and a low-cost manner. However, The tracker often
captures unnatural human poses such as discontinuous and vibrated motions when
self-occlusions occur. A majority of approaches tackle this problem by using
multiple Kin... | computer science |
1,156 | Deep Aesthetic Quality Assessment with Semantic Information | cs.CV | Human beings often assess the aesthetic quality of an image coupled with the
identification of the image's semantic content. This paper addresses the
correlation issue between automatic aesthetic quality assessment and semantic
recognition. We cast the assessment problem as the main task among a multi-task
deep model, ... | computer science |
1,157 | Deep Adaptive Network: An Efficient Deep Neural Network with Sparse
Binary Connections | cs.LG | Deep neural networks are state-of-the-art models for understanding the
content of images, video and raw input data. However, implementing a deep
neural network in embedded systems is a challenging task, because a typical
deep neural network, such as a Deep Belief Network using 128x128 images as
input, could exhaust Gig... | computer science |
1,158 | Image Colorization Using a Deep Convolutional Neural Network | cs.CV | In this paper, we present a novel approach that uses deep learning techniques
for colorizing grayscale images. By utilizing a pre-trained convolutional
neural network, which is originally designed for image classification, we are
able to separate content and style of different images and recombine them into
a single im... | computer science |
1,159 | Diving deeper into mentee networks | cs.LG | Modern computer vision is all about the possession of powerful image
representations. Deeper and deeper convolutional neural networks have been
built using larger and larger datasets and are made publicly available. A large
swath of computer vision scientists use these pre-trained networks with varying
degrees of succe... | computer science |
1,160 | Joint Line Segmentation and Transcription for End-to-End Handwritten
Paragraph Recognition | cs.CV | Offline handwriting recognition systems require cropped text line images for
both training and recognition. On the one hand, the annotation of position and
transcript at line level is costly to obtain. On the other hand, automatic line
segmentation algorithms are prone to errors, compromising the subsequent
recognition... | computer science |
1,161 | Learning Mid-Level Features and Modeling Neuron Selectivity for Image
Classification | cs.CV | We now know that mid-level features can greatly enhance the performance of
image learning, but how to automatically learn the image features efficiently
and in an unsupervised manner is still an open question. In this paper, we
present a very efficient mid-level feature learning approach (MidFea), which
only involves s... | computer science |
1,162 | Learning Multi-Scale Representations for Material Classification | cs.CV | The recent progress in sparse coding and deep learning has made unsupervised
feature learning methods a strong competitor to hand-crafted descriptors. In
computer vision, success stories of learned features have been predominantly
reported for object recognition tasks. In this paper, we investigate if and how
feature l... | computer science |
1,163 | Unsupervised Learning of Video Representations using LSTMs | cs.LG | We use multilayer Long Short Term Memory (LSTM) networks to learn
representations of video sequences. Our model uses an encoder LSTM to map an
input sequence into a fixed length representation. This representation is
decoded using single or multiple decoder LSTMs to perform different tasks, such
as reconstructing the i... | computer science |
1,164 | Evaluation of Deep Convolutional Nets for Document Image Classification
and Retrieval | cs.CV | This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive represe... | computer science |
1,165 | Deep Convolutional Inverse Graphics Network | cs.CV | This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a
model that learns an interpretable representation of images. This
representation is disentangled with respect to transformations such as
out-of-plane rotations and lighting variations. The DC-IGN model is composed of
multiple layers of convol... | computer science |
1,166 | Enhanced Image Classification With a Fast-Learning Shallow Convolutional
Neural Network | cs.NE | We present a neural network architecture and training method designed to
enable very rapid training and low implementation complexity. Due to its
training speed and very few tunable parameters, the method has strong potential
for applications requiring frequent retraining or online training. The approach
is characteriz... | computer science |
1,167 | Adversarial Images for Variational Autoencoders | cs.NE | We investigate adversarial attacks for autoencoders. We propose a procedure
that distorts the input image to mislead the autoencoder in reconstructing a
completely different target image. We attack the internal latent
representations, attempting to make the adversarial input produce an internal
representation as simila... | computer science |
1,168 | Parameter Compression of Recurrent Neural Networks and Degradation of
Short-term Memory | cs.CV | The significant computational costs of deploying neural networks in
large-scale or resource constrained environments, such as data centers and
mobile devices, has spurred interest in model compression, which can achieve a
reduction in both arithmetic operations and storage memory. Several techniques
have been proposed ... | computer science |
1,169 | Generalized Deep Image to Image Regression | cs.CV | We present a Deep Convolutional Neural Network architecture which serves as a
generic image-to-image regressor that can be trained end-to-end without any
further machinery. Our proposed architecture: the Recursively Branched
Deconvolutional Network (RBDN) develops a cheap multi-context image
representation very early o... | computer science |
1,170 | Exploring the Design Space of Deep Convolutional Neural Networks at
Large Scale | cs.CV | In recent years, the research community has discovered that deep neural
networks (DNNs) and convolutional neural networks (CNNs) can yield higher
accuracy than all previous solutions to a broad array of machine learning
problems. To our knowledge, there is no single CNN/DNN architecture that solves
all problems optimal... | computer science |
1,171 | Color and Shape Content Based Image Classification using RBF Network and
PSO Technique: A Survey | cs.CV | The improvement of the accuracy of image query retrieval used image
classification technique. Image classification is well known technique of
supervised learning. The improved method of image classification increases the
working efficiency of image query retrieval. For the improvements of
classification technique we us... | computer science |
1,172 | Rectifying Self Organizing Maps for Automatic Concept Learning from Web
Images | cs.CV | We attack the problem of learning concepts automatically from noisy web image
search results. Going beyond low level attributes, such as colour and texture,
we explore weakly-labelled datasets for the learning of higher level concepts,
such as scene categories. The idea is based on discovering common
characteristics sh... | computer science |
1,173 | Unsupervised feature learning by augmenting single images | cs.CV | When deep learning is applied to visual object recognition, data augmentation
is often used to generate additional training data without extra labeling cost.
It helps to reduce overfitting and increase the performance of the algorithm.
In this paper we investigate if it is possible to use data augmentation as the
main ... | computer science |
1,174 | Learning Paired-associate Images with An Unsupervised Deep Learning
Architecture | cs.NE | This paper presents an unsupervised multi-modal learning system that learns
associative representation from two input modalities, or channels, such that
input on one channel will correctly generate the associated response at the
other and vice versa. In this way, the system develops a kind of supervised
classification ... | computer science |
1,175 | Learning Human Pose Estimation Features with Convolutional Networks | cs.CV | This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our... | computer science |
1,176 | Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep
Object Recognition | cs.CV | We propose a general multi-class visual recognition model, termed the
Classifier Graph, which aims to generalize and integrate ideas from many of
today's successful hierarchical recognition approaches. Our graph-based model
has the advantage of enabling rich interactions between classes from different
levels of interpr... | computer science |
1,177 | PCANet: A Simple Deep Learning Baseline for Image Classification? | cs.CV | In this work, we propose a very simple deep learning network for image
classification which comprises only the very basic data processing components:
cascaded principal component analysis (PCA), binary hashing, and block-wise
histograms. In the proposed architecture, PCA is employed to learn multistage
filter banks. It... | computer science |
1,178 | Deep Metric Learning for Practical Person Re-Identification | cs.CV | Various hand-crafted features and metric learning methods prevail in the
field of person re-identification. Compared to these methods, this paper
proposes a more general way that can learn a similarity metric from image
pixels directly. By using a "siamese" deep neural network, the proposed method
can jointly learn the... | computer science |
1,179 | A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data | cs.CV | Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to deal with multimodal data, such as in image annotation
tasks. Another popular approach to model the multimodal data is through deep
neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type
of topic model c... | computer science |
1,180 | MoDeep: A Deep Learning Framework Using Motion Features for Human Pose
Estimation | cs.CV | In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We ap... | computer science |
1,181 | Scale-Invariant Convolutional Neural Networks | cs.CV | Even though convolutional neural networks (CNN) has achieved near-human
performance in various computer vision tasks, its ability to tolerate scale
variations is limited. The popular practise is making the model bigger first,
and then train it with data augmentation using extensive scale-jittering. In
this paper, we pr... | computer science |
1,182 | MatConvNet - Convolutional Neural Networks for MATLAB | cs.CV | MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for
MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility.
It exposes the building blocks of CNNs as easy-to-use MATLAB functions,
providing routines for computing linear convolutions with filter banks, feature
pooling, and... | computer science |
1,183 | Discovering Hidden Factors of Variation in Deep Networks | cs.LG | Deep learning has enjoyed a great deal of success because of its ability to
learn useful features for tasks such as classification. But there has been less
exploration in learning the factors of variation apart from the classification
signal. By augmenting autoencoders with simple regularization terms during
training, ... | computer science |
1,184 | An Analysis of Unsupervised Pre-training in Light of Recent Advances | cs.CV | Convolutional neural networks perform well on object recognition because of a
number of recent advances: rectified linear units (ReLUs), data augmentation,
dropout, and large labelled datasets. Unsupervised data has been proposed as
another way to improve performance. Unfortunately, unsupervised pre-training is
not use... | computer science |
1,185 | Permutohedral Lattice CNNs | cs.CV | This paper presents a convolutional layer that is able to process sparse
input features. As an example, for image recognition problems this allows an
efficient filtering of signals that do not lie on a dense grid (like pixel
position), but of more general features (such as color values). The presented
algorithm makes u... | computer science |
1,186 | Striving for Simplicity: The All Convolutional Net | cs.LG | Most modern convolutional neural networks (CNNs) used for object recognition
are built using the same principles: Alternating convolution and max-pooling
layers followed by a small number of fully connected layers. We re-evaluate the
state of the art for object recognition from small images with convolutional
networks,... | computer science |
1,187 | Half-CNN: A General Framework for Whole-Image Regression | cs.CV | The Convolutional Neural Network (CNN) has achieved great success in image
classification. The classification model can also be utilized at image or patch
level for many other applications, such as object detection and segmentation.
In this paper, we propose a whole-image CNN regression model, by removing the
full conn... | computer science |
1,188 | Occlusion Edge Detection in RGB-D Frames using Deep Convolutional
Networks | cs.CV | Occlusion edges in images which correspond to range discontinuity in the
scene from the point of view of the observer are an important prerequisite for
many vision and mobile robot tasks. Although they can be extracted from range
data however extracting them from images and videos would be extremely
beneficial. We trai... | computer science |
1,189 | Training deep neural networks with low precision multiplications | cs.LG | Multipliers are the most space and power-hungry arithmetic operators of the
digital implementation of deep neural networks. We train a set of
state-of-the-art neural networks (Maxout networks) on three benchmark datasets:
MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats:
floating point, fixed poin... | computer science |
1,190 | Semantic Image Segmentation with Deep Convolutional Nets and Fully
Connected CRFs | cs.CV | Deep Convolutional Neural Networks (DCNNs) have recently shown state of the
art performance in high level vision tasks, such as image classification and
object detection. This work brings together methods from DCNNs and
probabilistic graphical models for addressing the task of pixel-level
classification (also called "s... | computer science |
1,191 | Learning Deep Object Detectors from 3D Models | cs.CV | Crowdsourced 3D CAD models are becoming easily accessible online, and can
potentially generate an infinite number of training images for almost any
object category.We show that augmenting the training data of contemporary Deep
Convolutional Neural Net (DCNN) models with such synthetic data can be
effective, especially ... | computer science |
1,192 | Learning Compact Convolutional Neural Networks with Nested Dropout | cs.CV | Recently, nested dropout was proposed as a method for ordering representation
units in autoencoders by their information content, without diminishing
reconstruction cost. However, it has only been applied to training
fully-connected autoencoders in an unsupervised setting. We explore the impact
of nested dropout on the... | computer science |
1,193 | Constrained Extreme Learning Machines: A Study on Classification Cases | cs.LG | Extreme learning machine (ELM) is an extremely fast learning method and has a
powerful performance for pattern recognition tasks proven by enormous
researches and engineers. However, its good generalization ability is built on
large numbers of hidden neurons, which is not beneficial to real time response
in the test pr... | computer science |
1,194 | Pixel-wise Deep Learning for Contour Detection | cs.CV | We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM class... | computer science |
1,195 | When Face Recognition Meets with Deep Learning: an Evaluation of
Convolutional Neural Networks for Face Recognition | cs.CV | Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognit... | computer science |
1,196 | Learning to Compare Image Patches via Convolutional Neural Networks | cs.CV | In this paper we show how to learn directly from image data (i.e., without
resorting to manually-designed features) a general similarity function for
comparing image patches, which is a task of fundamental importance for many
computer vision problems. To encode such a function, we opt for a CNN-based
model that is trai... | computer science |
1,197 | Predicting Deep Zero-Shot Convolutional Neural Networks using Textual
Descriptions | cs.LG | One of the main challenges in Zero-Shot Learning of visual categories is
gathering semantic attributes to accompany images. Recent work has shown that
learning from textual descriptions, such as Wikipedia articles, avoids the
problem of having to explicitly define these attributes. We present a new model
that can class... | computer science |
1,198 | Cyclical Learning Rates for Training Neural Networks | cs.CV | It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global lea... | computer science |
1,199 | Learning both Weights and Connections for Efficient Neural Networks | cs.NE | Neural networks are both computationally intensive and memory intensive,
making them difficult to deploy on embedded systems. Also, conventional
networks fix the architecture before training starts; as a result, training
cannot improve the architecture. To address these limitations, we describe a
method to reduce the s... | computer science |
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