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1,300 | DRAW: A Recurrent Neural Network For Image Generation | cs.CV | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of com... | computer science |
1,301 | Training Binary Multilayer Neural Networks for Image Classification
using Expectation Backpropagation | cs.NE | Compared to Multilayer Neural Networks with real weights, Binary Multilayer
Neural Networks (BMNNs) can be implemented more efficiently on dedicated
hardware. BMNNs have been demonstrated to be effective on binary classification
tasks with Expectation BackPropagation (EBP) algorithm on high dimensional text
datasets. I... | computer science |
1,302 | Towards the Limit of Network Quantization | cs.CV | Network quantization is one of network compression techniques to reduce the
redundancy of deep neural networks. It reduces the number of distinct network
parameter values by quantization in order to save the storage for them. In this
paper, we design network quantization schemes that minimize the performance
loss due t... | computer science |
1,303 | EgoTransfer: Transferring Motion Across Egocentric and Exocentric
Domains using Deep Neural Networks | cs.CV | Mirror neurons have been observed in the primary motor cortex of primate
species, in particular in humans and monkeys. A mirror neuron fires when a
person performs a certain action, and also when he observes the same action
being performed by another person. A crucial step towards building fully
autonomous intelligent ... | computer science |
1,304 | Learning from Simulated and Unsupervised Images through Adversarial
Training | cs.CV | With recent progress in graphics, it has become more tractable to train
models on synthetic images, potentially avoiding the need for expensive
annotations. However, learning from synthetic images may not achieve the
desired performance due to a gap between synthetic and real image
distributions. To reduce this gap, we... | computer science |
1,305 | Spatially-Adaptive Reconstruction in Computed Tomography using Neural
Networks | cs.CV | We propose a supervised machine learning approach for boosting existing
signal and image recovery methods and demonstrate its efficacy on example of
image reconstruction in computed tomography. Our technique is based on a local
nonlinear fusion of several image estimates, all obtained by applying a chosen
reconstructio... | computer science |
1,306 | Network In Network | cs.NE | We propose a novel deep network structure called "Network In Network" (NIN)
to enhance model discriminability for local patches within the receptive field.
The conventional convolutional layer uses linear filters followed by a
nonlinear activation function to scan the input. Instead, we build micro neural
networks with... | computer science |
1,307 | Dropout improves Recurrent Neural Networks for Handwriting Recognition | cs.CV | Recurrent neural networks (RNNs) with Long Short-Term memory cells currently
hold the best known results in unconstrained handwriting recognition. We show
that their performance can be greatly improved using dropout - a recently
proposed regularization method for deep architectures. While previous works
showed that dro... | computer science |
1,308 | Unsupervised Feature Learning by Deep Sparse Coding | cs.LG | In this paper, we propose a new unsupervised feature learning framework,
namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer
architecture for visual object recognition tasks. The main innovation of the
framework is that it connects the sparse-encoders from different layers by a
sparse-to-den... | computer science |
1,309 | Fast Training of Convolutional Networks through FFTs | cs.CV | Convolutional networks are one of the most widely employed architectures in
computer vision and machine learning. In order to leverage their ability to
learn complex functions, large amounts of data are required for training.
Training a large convolutional network to produce state-of-the-art results can
take weeks, eve... | computer science |
1,310 | Deep Belief Networks for Image Denoising | cs.LG | Deep Belief Networks which are hierarchical generative models are effective
tools for feature representation and extraction. Furthermore, DBNs can be used
in numerous aspects of Machine Learning such as image denoising. In this paper,
we propose a novel method for image denoising which relies on the DBNs' ability
in fe... | computer science |
1,311 | GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network
Training | cs.CV | The ability to train large-scale neural networks has resulted in
state-of-the-art performance in many areas of computer vision. These results
have largely come from computational break throughs of two forms: model
parallelism, e.g. GPU accelerated training, which has seen quick adoption in
computer vision circles, and ... | computer science |
1,312 | Intriguing properties of neural networks | cs.CV | Deep neural networks are highly expressive models that have recently achieved
state of the art performance on speech and visual recognition tasks. While
their expressiveness is the reason they succeed, it also causes them to learn
uninterpretable solutions that could have counter-intuitive properties. In this
paper we ... | computer science |
1,313 | Spectral Networks and Locally Connected Networks on Graphs | cs.LG | Convolutional Neural Networks are extremely efficient architectures in image
and audio recognition tasks, thanks to their ability to exploit the local
translational invariance of signal classes over their domain. In this paper we
consider possible generalizations of CNNs to signals defined on more general
domains witho... | computer science |
1,314 | One-Shot Adaptation of Supervised Deep Convolutional Models | cs.CV | Dataset bias remains a significant barrier towards solving real world
computer vision tasks. Though deep convolutional networks have proven to be a
competitive approach for image classification, a question remains: have these
models have solved the dataset bias problem? In general, training or
fine-tuning a state-of-th... | computer science |
1,315 | Deep learning for class-generic object detection | cs.CV | We investigate the use of deep neural networks for the novel task of class
generic object detection. We show that neural networks originally designed for
image recognition can be trained to detect objects within images, regardless of
their class, including objects for which no bounding box labels have been
provided. In... | computer science |
1,316 | Deep Networks with Internal Selective Attention through Feedback
Connections | cs.CV | Traditional convolutional neural networks (CNN) are stationary and
feedforward. They neither change their parameters during evaluation nor use
feedback from higher to lower layers. Real brains, however, do. So does our
Deep Attention Selective Network (dasNet) architecture. DasNets feedback
structure can dynamically al... | computer science |
1,317 | Efficient On-the-fly Category Retrieval using ConvNets and GPUs | cs.CV | We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
We make three contributions: (i) we present a... | computer science |
1,318 | Computing the Stereo Matching Cost with a Convolutional Neural Network | cs.CV | We present a method for extracting depth information from a rectified image
pair. We train a convolutional neural network to predict how well two image
patches match and use it to compute the stereo matching cost. The cost is
refined by cross-based cost aggregation and semiglobal matching, followed by a
left-right cons... | computer science |
1,319 | HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale
Visual Recognition | cs.CV | In image classification, visual separability between different object
categories is highly uneven, and some categories are more difficult to
distinguish than others. Such difficult categories demand more dedicated
classifiers. However, existing deep convolutional neural networks (CNN) are
trained as flat N-way classifi... | computer science |
1,320 | DeepSentiBank: Visual Sentiment Concept Classification with Deep
Convolutional Neural Networks | cs.CV | This paper introduces a visual sentiment concept classification method based
on deep convolutional neural networks (CNNs). The visual sentiment concepts are
adjective noun pairs (ANPs) automatically discovered from the tags of web
photos, and can be utilized as effective statistical cues for detecting
emotions depicted... | computer science |
1,321 | Do Convnets Learn Correspondence? | cs.CV | Convolutional neural nets (convnets) trained from massive labeled datasets
have substantially improved the state-of-the-art in image classification and
object detection. However, visual understanding requires establishing
correspondence on a finer level than object category. Given their large pooling
regions and traini... | computer science |
1,322 | Convolutional Neural Network-based Place Recognition | cs.CV | Recently Convolutional Neural Networks (CNNs) have been shown to achieve
state-of-the-art performance on various classification tasks. In this paper, we
present for the first time a place recognition technique based on CNN models,
by combining the powerful features learnt by CNNs with a spatial and sequential
filter. A... | computer science |
1,323 | Predictive Encoding of Contextual Relationships for Perceptual
Inference, Interpolation and Prediction | cs.LG | We propose a new neurally-inspired model that can learn to encode the global
relationship context of visual events across time and space and to use the
contextual information to modulate the analysis by synthesis process in a
predictive coding framework. The model learns latent contextual representations
by maximizing ... | computer science |
1,324 | Understanding image representations by measuring their equivariance and
equivalence | cs.CV | Despite the importance of image representations such as histograms of
oriented gradients and deep Convolutional Neural Networks (CNN), our
theoretical understanding of them remains limited. Aiming at filling this gap,
we investigate three key mathematical properties of representations:
equivariance, invariance, and equ... | computer science |
1,325 | Learning to Generate Chairs, Tables and Cars with Convolutional Networks | cs.CV | We train generative 'up-convolutional' neural networks which are able to
generate images of objects given object style, viewpoint, and color. We train
the networks on rendered 3D models of chairs, tables, and cars. Our experiments
show that the networks do not merely learn all images by heart, but rather find
a meaning... | computer science |
1,326 | Fisher Kernel for Deep Neural Activations | cs.CV | Compared to image representation based on low-level local descriptors, deep
neural activations of Convolutional Neural Networks (CNNs) are richer in
mid-level representation, but poorer in geometric invariance properties. In
this paper, we present a straightforward framework for better image
representation by combining... | computer science |
1,327 | Object Recognition Using Deep Neural Networks: A Survey | cs.CV | Recognition of objects using Deep Neural Networks is an active area of
research and many breakthroughs have been made in the last few years. The paper
attempts to indicate how far this field has progressed. The paper briefly
describes the history of research in Neural Networks and describe several of
the recent advance... | computer science |
1,328 | Towards Deep Neural Network Architectures Robust to Adversarial Examples | cs.LG | Recent work has shown deep neural networks (DNNs) to be highly susceptible to
well-designed, small perturbations at the input layer, or so-called adversarial
examples. Taking images as an example, such distortions are often
imperceptible, but can result in 100% mis-classification for a state of the art
DNN. We study th... | computer science |
1,329 | Locally Scale-Invariant Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (ConvNets) have shown excellent results on many
visual classification tasks. With the exception of ImageNet, these datasets are
carefully crafted such that objects are well-aligned at similar scales.
Naturally, the feature learning problem gets more challenging as the amount of
variation i... | computer science |
1,330 | Compressing Deep Convolutional Networks using Vector Quantization | cs.CV | Deep convolutional neural networks (CNN) has become the most promising method
for object recognition, repeatedly demonstrating record breaking results for
image classification and object detection in recent years. However, a very deep
CNN generally involves many layers with millions of parameters, making the
storage of... | computer science |
1,331 | Generative Modeling of Convolutional Neural Networks | cs.CV | The convolutional neural networks (CNNs) have proven to be a powerful tool
for discriminative learning. Recently researchers have also started to show
interest in the generative aspects of CNNs in order to gain a deeper
understanding of what they have learned and how to further improve them. This
paper investigates gen... | computer science |
1,332 | Training Deep Neural Networks on Noisy Labels with Bootstrapping | cs.CV | Current state-of-the-art deep learning systems for visual object recognition
and detection use purely supervised training with regularization such as
dropout to avoid overfitting. The performance depends critically on the amount
of labeled examples, and in current practice the labels are assumed to be
unambiguous and a... | computer science |
1,333 | Contour Detection Using Cost-Sensitive Convolutional Neural Networks | 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 |
1,334 | Multi-modal Sensor Registration for Vehicle Perception via Deep Neural
Networks | cs.CV | The ability to simultaneously leverage multiple modes of sensor information
is critical for perception of an automated vehicle's physical surroundings.
Spatio-temporal alignment of registration of the incoming information is often
a prerequisite to analyzing the fused data. The persistence and reliability of
multi-moda... | computer science |
1,335 | Attention for Fine-Grained Categorization | cs.CV | This paper presents experiments extending the work of Ba et al. (2014) on
recurrent neural models for attention into less constrained visual
environments, specifically fine-grained categorization on the Stanford Dogs
data set. In this work we use an RNN of the same structure but substitute a
more powerful visual networ... | computer science |
1,336 | Fully Convolutional Multi-Class Multiple Instance Learning | cs.CV | Multiple instance learning (MIL) can reduce the need for costly annotation in
tasks such as semantic segmentation by weakening the required degree of
supervision. We propose a novel MIL formulation of multi-class semantic
segmentation learning by a fully convolutional network. In this setting, we
seek to learn a semant... | computer science |
1,337 | Convolutional Neural Networks for joint object detection and pose
estimation: A comparative study | cs.CV | In this paper we study the application of convolutional neural networks for
jointly detecting objects depicted in still images and estimating their 3D
pose. We identify different feature representations of oriented objects, and
energies that lead a network to learn this representations. The choice of the
representation... | computer science |
1,338 | Unsupervised Feature Learning with C-SVDDNet | cs.CV | In this paper, we investigate the problem of learning feature representation
from unlabeled data using a single-layer K-means network. A K-means network
maps the input data into a feature representation by finding the nearest
centroid for each input point, which has attracted researchers' great attention
recently due t... | computer science |
1,339 | Transformation Properties of Learned Visual Representations | cs.LG | When a three-dimensional object moves relative to an observer, a change
occurs on the observer's image plane and in the visual representation computed
by a learned model. Starting with the idea that a good visual representation is
one that transforms linearly under scene motions, we show, using the theory of
group repr... | computer science |
1,340 | Multiple Object Recognition with Visual Attention | cs.LG | We present an attention-based model for recognizing multiple objects in
images. The proposed model is a deep recurrent neural network trained with
reinforcement learning to attend to the most relevant regions of the input
image. We show that the model learns to both localize and recognize multiple
objects despite being... | computer science |
1,341 | Fast, simple and accurate handwritten digit classification by training
shallow neural network classifiers with the 'extreme learning machine'
algorithm | cs.NE | Recent advances in training deep (multi-layer) architectures have inspired a
renaissance in neural network use. For example, deep convolutional networks are
becoming the default option for difficult tasks on large datasets, such as
image and speech recognition. However, here we show that error rates below 1%
on the MNI... | computer science |
1,342 | Sparse Deep Stacking Network for Image Classification | cs.CV | Sparse coding can learn good robust representation to noise and model more
higher-order representation for image classification. However, the inference
algorithm is computationally expensive even though the supervised signals are
used to learn compact and discriminative dictionaries in sparse coding
techniques. Luckily... | computer science |
1,343 | Implementation of Training Convolutional Neural Networks | cs.CV | Deep learning refers to the shining branch of machine learning that is based
on learning levels of representations. Convolutional Neural Networks (CNN) is
one kind of deep neural network. It can study concurrently. In this article, we
gave a detailed analysis of the process of CNN algorithm both the forward
process and... | computer science |
1,344 | Inverting Visual Representations with Convolutional Networks | cs.NE | Feature representations, both hand-designed and learned ones, are often hard
to analyze and interpret, even when they are extracted from visual data. We
propose a new approach to study image representations by inverting them with an
up-convolutional neural network. We apply the method to shallow representations
(HOG, S... | computer science |
1,345 | Place classification with a graph regularized deep neural network model | cs.RO | Place classification is a fundamental ability that a robot should possess to
carry out effective human-robot interactions. It is a nontrivial classification
problem which has attracted many research. In recent years, there is a high
exploitation of Artificial Intelligent algorithms in robotics applications.
Inspired by... | computer science |
1,346 | Deep Convolutional Networks on Graph-Structured Data | cs.LG | Deep Learning's recent successes have mostly relied on Convolutional
Networks, which exploit fundamental statistical properties of images, sounds
and video data: the local stationarity and multi-scale compositional structure,
that allows expressing long range interactions in terms of shorter, localized
interactions. Ho... | computer science |
1,347 | End-to-end Convolutional Network for Saliency Prediction | cs.CV | The prediction of saliency areas in images has been traditionally addressed
with hand crafted features based on neuroscience principles. This paper however
addresses the problem with a completely data-driven approach by training a
convolutional network. The learning process is formulated as a minimization of
a loss fun... | computer science |
1,348 | Human Pose Estimation with Iterative Error Feedback | cs.CV | Hierarchical feature extractors such as Convolutional Networks (ConvNets)
have achieved impressive performance on a variety of classification tasks using
purely feedforward processing. Feedforward architectures can learn rich
representations of the input space but do not explicitly model dependencies in
the output spac... | computer science |
1,349 | Multimodal Deep Learning for Robust RGB-D Object Recognition | cs.CV | Robust object recognition is a crucial ingredient of many, if not all,
real-world robotics applications. This paper leverages recent progress on
Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture
for object recognition. Our architecture is composed of two separate CNN
processing streams - one ... | computer science |
1,350 | Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning
in Hierarchy | cs.CV | This work investigates how the traditional image classification pipelines can
be extended into a deep architecture, inspired by recent successes of deep
neural networks. We propose a deep boosting framework based on layer-by-layer
joint feature boosting and dictionary learning. In each layer, we construct a
dictionary ... | computer science |
1,351 | StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity | cs.CV | Deep neural networks is a branch in machine learning that has seen a meteoric
rise in popularity due to its powerful abilities to represent and model
high-level abstractions in highly complex data. One area in deep neural
networks that is ripe for exploration is neural connectivity formation. A
pivotal study on the bra... | computer science |
1,352 | Deep Convolutional Neural Networks for Smile Recognition | cs.CV | This thesis describes the design and implementation of a smile detector based
on deep convolutional neural networks. It starts with a summary of neural
networks, the difficulties of training them and new training methods, such as
Restricted Boltzmann Machines or autoencoders. It then provides a literature
review of con... | computer science |
1,353 | Towards universal neural nets: Gibbs machines and ACE | cs.CV | We study from a physics viewpoint a class of generative neural nets, Gibbs
machines, designed for gradual learning. While including variational
auto-encoders, they offer a broader universal platform for incrementally adding
newly learned features, including physical symmetries. Their direct connection
to statistical ph... | computer science |
1,354 | Rapid Exact Signal Scanning with Deep Convolutional Neural Networks | cs.LG | A rigorous formulation of the dynamics of a signal processing scheme aimed at
dense signal scanning without any loss in accuracy is introduced and analyzed.
Related methods proposed in the recent past lack a satisfactory analysis of
whether they actually fulfill any exactness constraints. This is improved
through an ex... | computer science |
1,355 | Hierarchical Deep Learning Architecture For 10K Objects Classification | cs.CV | Evolution of visual object recognition architectures based on Convolutional
Neural Networks & Convolutional Deep Belief Networks paradigms has
revolutionized artificial Vision Science. These architectures extract & learn
the real world hierarchical visual features utilizing supervised & unsupervised
learning approaches... | computer science |
1,356 | Deep Attributes from Context-Aware Regional Neural Codes | cs.CV | Recently, many researches employ middle-layer output of convolutional neural
network models (CNN) as features for different visual recognition tasks.
Although promising results have been achieved in some empirical studies, such
type of representations still suffer from the well-known issue of semantic gap.
This paper p... | computer science |
1,357 | Deep Trans-layer Unsupervised Networks for Representation Learning | cs.NE | Learning features from massive unlabelled data is a vast prevalent topic for
high-level tasks in many machine learning applications. The recent great
improvements on benchmark data sets achieved by increasingly complex
unsupervised learning methods and deep learning models with lots of parameters
usually requires many ... | computer science |
1,358 | Compression of Deep Neural Networks on the Fly | cs.LG | Thanks to their state-of-the-art performance, deep neural networks are
increasingly used for object recognition. To achieve these results, they use
millions of parameters to be trained. However, when targeting embedded
applications the size of these models becomes problematic. As a consequence,
their usage on smartphon... | computer science |
1,359 | BinaryConnect: Training Deep Neural Networks with binary weights during
propagations | cs.LG | Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide
range of tasks, with the best results obtained with large training sets and
large models. In the past, GPUs enabled these breakthroughs because of their
greater computational speed. In the future, faster computation at both training
and test ti... | computer science |
1,360 | Learning Human Identity from Motion Patterns | cs.LG | We present a large-scale study exploring the capability of temporal deep
neural networks to interpret natural human kinematics and introduce the first
method for active biometric authentication with mobile inertial sensors. At
Google, we have created a first-of-its-kind dataset of human movements,
passively collected b... | computer science |
1,361 | Efficient Training of Very Deep Neural Networks for Supervised Hashing | cs.CV | In this paper, we propose training very deep neural networks (DNNs) for
supervised learning of hash codes. Existing methods in this context train
relatively "shallow" networks limited by the issues arising in back propagation
(e.e. vanishing gradients) as well as computational efficiency. We propose a
novel and efficie... | computer science |
1,362 | Adversarial Manipulation of Deep Representations | cs.CV | We show that the representation of an image in a deep neural network (DNN)
can be manipulated to mimic those of other natural images, with only minor,
imperceptible perturbations to the original image. Previous methods for
generating adversarial images focused on image perturbations designed to
produce erroneous class ... | computer science |
1,363 | Identifying the Absorption Bump with Deep Learning | cs.CV | The pervasive interstellar dust grains provide significant insights to
understand the formation and evolution of the stars, planetary systems, and the
galaxies, and may harbor the building blocks of life. One of the most effective
way to analyze the dust is via their interaction with the light from background
sources. ... | computer science |
1,364 | Competitive Multi-scale Convolution | cs.CV | In this paper, we introduce a new deep convolutional neural network (ConvNet)
module that promotes competition among a set of multi-scale convolutional
filters. This new module is inspired by the inception module, where we replace
the original collaborative pooling stage (consisting of a concatenation of the
multi-scal... | computer science |
1,365 | Semi-supervised Learning for Convolutional Neural Networks via Online
Graph Construction | cs.NE | The recent promising achievements of deep learning rely on the large amount
of labeled data. Considering the abundance of data on the web, most of them do
not have labels at all. Therefore, it is important to improve generalization
performance using unlabeled data on supervised tasks with few labeled
instances. In this... | computer science |
1,366 | How much data is needed to train a medical image deep learning system to
achieve necessary high accuracy? | cs.LG | The use of Convolutional Neural Networks (CNN) in natural image
classification systems has produced very impressive results. Combined with the
inherent nature of medical images that make them ideal for deep-learning,
further application of such systems to medical image classification holds much
promise. However, the us... | computer science |
1,367 | Delving Deeper into Convolutional Networks for Learning Video
Representations | cs.CV | We propose an approach to learn spatio-temporal features in videos from
intermediate visual representations we call "percepts" using
Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts
that are extracted from all level of a deep convolutional network trained on
the large ImageNet dataset. While... | computer science |
1,368 | An Introduction to Convolutional Neural Networks | cs.NE | The field of machine learning has taken a dramatic twist in recent times,
with the rise of the Artificial Neural Network (ANN). These biologically
inspired computational models are able to far exceed the performance of
previous forms of artificial intelligence in common machine learning tasks. One
of the most impressiv... | computer science |
1,369 | Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views | cs.CV | This paper presents an end-to-end convolutional neural network (CNN) for
2D-3D exemplar detection. We demonstrate that the ability to adapt the features
of natural images to better align with those of CAD rendered views is critical
to the success of our technique. We show that the adaptation can be learned by
compositi... | computer science |
1,370 | Pixel Recurrent Neural Networks | cs.CV | Modeling the distribution of natural images is a landmark problem in
unsupervised learning. This task requires an image model that is at once
expressive, tractable and scalable. We present a deep neural network that
sequentially predicts the pixels in an image along the two spatial dimensions.
Our method models the dis... | computer science |
1,371 | Deep Learning For Smile Recognition | cs.CV | Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) ... | computer science |
1,372 | Generating Images with Perceptual Similarity Metrics based on Deep
Networks | cs.LG | Image-generating machine learning models are typically trained with loss
functions based on distance in the image space. This often leads to
over-smoothed results. We propose a class of loss functions, which we call deep
perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of
computing distances... | computer science |
1,373 | Exploiting Cyclic Symmetry in Convolutional Neural Networks | cs.LG | Many classes of images exhibit rotational symmetry. Convolutional neural
networks are sometimes trained using data augmentation to exploit this, but
they are still required to learn the rotation equivariance properties from the
data. Encoding these properties into the network architecture, as we are
already used to doi... | computer science |
1,374 | The Role of Typicality in Object Classification: Improving The
Generalization Capacity of Convolutional Neural Networks | cs.CV | Deep artificial neural networks have made remarkable progress in different
tasks in the field of computer vision. However, the empirical analysis of these
models and investigation of their failure cases has received attention
recently. In this work, we show that deep learning models cannot generalize to
atypical images... | computer science |
1,375 | On Study of the Binarized Deep Neural Network for Image Classification | cs.NE | Recently, the deep neural network (derived from the artificial neural
network) has attracted many researchers' attention by its outstanding
performance. However, since this network requires high-performance GPUs and
large storage, it is very hard to use it on individual devices. In order to
improve the deep neural netw... | computer science |
1,376 | A Single Model Explains both Visual and Auditory Precortical Coding | cs.CV | Precortical neural systems encode information collected by the senses, but
the driving principles of the encoding used have remained a subject of debate.
We present a model of retinal coding that is based on three constraints:
information preservation, minimization of the neural wiring, and response
equalization. The r... | computer science |
1,377 | Network Morphism | cs.LG | We present in this paper a systematic study on how to morph a well-trained
neural network to a new one so that its network function can be completely
preserved. We define this as \emph{network morphism} in this research. After
morphing a parent network, the child network is expected to inherit the
knowledge from its pa... | computer science |
1,378 | DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range
Data | cs.RO | We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated... | computer science |
1,379 | Efficient forward propagation of time-sequences in convolutional neural
networks using Deep Shifting | cs.LG | When a Convolutional Neural Network is used for on-the-fly evaluation of
continuously updating time-sequences, many redundant convolution operations are
performed. We propose the method of Deep Shifting, which remembers previously
calculated results of convolution operations in order to minimize the number of
calculati... | computer science |
1,380 | Evolution of active categorical image classification via saccadic eye
movement | cs.CV | Pattern recognition and classification is a central concern for modern
information processing systems. In particular, one key challenge to image and
video classification has been that the computational cost of image processing
scales linearly with the number of pixels in the image or video. Here we
present an intellige... | computer science |
1,381 | Not Just a Black Box: Learning Important Features Through Propagating
Activation Differences | cs.LG | Note: This paper describes an older version of DeepLIFT. See
https://arxiv.org/abs/1704.02685 for the newer version. Original abstract
follows: The purported "black box" nature of neural networks is a barrier to
adoption in applications where interpretability is essential. Here we present
DeepLIFT (Learning Important F... | computer science |
1,382 | LightNet: A Versatile, Standalone Matlab-based Environment for Deep
Learning | cs.LG | LightNet is a lightweight, versatile and purely Matlab-based deep learning
framework. The idea underlying its design is to provide an easy-to-understand,
easy-to-use and efficient computational platform for deep learning research.
The implemented framework supports major deep learning architectures such as
Multilayer P... | computer science |
1,383 | Deep Action Sequence Learning for Causal Shape Transformation | cs.LG | Deep learning became the method of choice in recent year for solving a wide
variety of predictive analytics tasks. For sequence prediction, recurrent
neural networks (RNN) are often the go-to architecture for exploiting
sequential information where the output is dependent on previous computation.
However, the dependenc... | computer science |
1,384 | Swapout: Learning an ensemble of deep architectures | cs.CV | We describe Swapout, a new stochastic training method, that outperforms
ResNets of identical network structure yielding impressive results on CIFAR-10
and CIFAR-100. Swapout samples from a rich set of architectures including
dropout, stochastic depth and residual architectures as special cases. When
viewed as a regular... | computer science |
1,385 | Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups | cs.NE | We propose a new method for creating computationally efficient and compact
convolutional neural networks (CNNs) using a novel sparse connection structure
that resembles a tree root. This allows a significant reduction in
computational cost and number of parameters compared to state-of-the-art deep
CNNs, without comprom... | computer science |
1,386 | Wide Residual Networks | cs.CV | Deep residual networks were shown to be able to scale up to thousands of
layers and still have improving performance. However, each fraction of a
percent of improved accuracy costs nearly doubling the number of layers, and so
training very deep residual networks has a problem of diminishing feature
reuse, which makes t... | computer science |
1,387 | Measuring Neural Net Robustness with Constraints | cs.LG | Despite having high accuracy, neural nets have been shown to be susceptible
to adversarial examples, where a small perturbation to an input can cause it to
become mislabeled. We propose metrics for measuring the robustness of a neural
net and devise a novel algorithm for approximating these metrics based on an
encoding... | computer science |
1,388 | Parametric Exponential Linear Unit for Deep Convolutional Neural
Networks | cs.LG | Object recognition is an important task for improving the ability of visual
systems to perform complex scene understanding. Recently, the Exponential
Linear Unit (ELU) has been proposed as a key component for managing bias shift
in Convolutional Neural Networks (CNNs), but defines a parameter that must be
set by hand. ... | computer science |
1,389 | Deep convolutional neural networks for predominant instrument
recognition in polyphonic music | cs.SD | Identifying musical instruments in polyphonic music recordings is a
challenging but important problem in the field of music information retrieval.
It enables music search by instrument, helps recognize musical genres, or can
make music transcription easier and more accurate. In this paper, we present a
convolutional ne... | computer science |
1,390 | Recursive Autoconvolution for Unsupervised Learning of Convolutional
Neural Networks | cs.CV | In visual recognition tasks, such as image classification, unsupervised
learning exploits cheap unlabeled data and can help to solve these tasks more
efficiently. We show that the recursive autoconvolution operator, adopted from
physics, boosts existing unsupervised methods by learning more discriminative
filters. We t... | computer science |
1,391 | Generalizing the Convolution Operator to extend CNNs to Irregular
Domains | cs.LG | Convolutional Neural Networks (CNNs) have become the state-of-the-art in
supervised learning vision tasks. Their convolutional filters are of paramount
importance for they allow to learn patterns while disregarding their locations
in input images. When facing highly irregular domains, generalized
convolutional operator... | computer science |
1,392 | Deep neural networks are robust to weight binarization and other
non-linear distortions | cs.NE | Recent results show that deep neural networks achieve excellent performance
even when, during training, weights are quantized and projected to a binary
representation. Here, we show that this is just the tip of the iceberg: these
same networks, during testing, also exhibit a remarkable robustness to
distortions beyond ... | computer science |
1,393 | Systematic evaluation of CNN advances on the ImageNet | cs.NE | The paper systematically studies the impact of a range of recent advances in
CNN architectures and learning methods on the object categorization (ILSVRC)
problem. The evalution tests the influence of the following choices of the
architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), po... | computer science |
1,394 | Convolutional Neural Fabrics | cs.CV | Despite the success of CNNs, selecting the optimal architecture for a given
task remains an open problem. Instead of aiming to select a single optimal
architecture, we propose a "fabric" that embeds an exponentially large number
of architectures. The fabric consists of a 3D trellis that connects response
maps at differ... | computer science |
1,395 | Convolution by Evolution: Differentiable Pattern Producing Networks | cs.NE | In this work we introduce a differentiable version of the Compositional
Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the
topology of a DPPN is evolved but the weights are learned. A Lamarckian
algorithm, that combines evolution and learning, produces DPPNs to reconstruct
an image. Our main result... | computer science |
1,396 | Inverting face embeddings with convolutional neural networks | cs.CV | Deep neural networks have dramatically advanced the state of the art for many
areas of machine learning. Recently they have been shown to have a remarkable
ability to generate highly complex visual artifacts such as images and text
rather than simply recognize them.
In this work we use neural networks to effectively ... | computer science |
1,397 | Sequence to sequence learning for unconstrained scene text recognition | cs.CV | In this work we present a state-of-the-art approach for unconstrained natural
scene text recognition. We propose a cascade approach that incorporates a
convolutional neural network (CNN) architecture followed by a long short term
memory model (LSTM). The CNN learns visual features for the characters and uses
them with ... | computer science |
1,398 | Hierarchical Multi-resolution Mesh Networks for Brain Decoding | cs.NE | We propose a new framework, called Hierarchical Multi-resolution Mesh
Networks (HMMNs), which establishes a set of brain networks at multiple time
resolutions of fMRI signal to represent the underlying cognitive process. The
suggested framework, first, decomposes the fMRI signal into various frequency
subbands using wa... | computer science |
1,399 | Early Methods for Detecting Adversarial Images | cs.LG | Many machine learning classifiers are vulnerable to adversarial
perturbations. An adversarial perturbation modifies an input to change a
classifier's prediction without causing the input to seem substantially
different to human perception. We deploy three methods to detect adversarial
images. Adversaries trying to bypa... | computer science |
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