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3,500 | Class-Splitting Generative Adversarial Networks | stat.ML | Generative Adversarial Networks (GANs) produce systematically better quality
samples when class label information is provided., i.e. in the conditional GAN
setup. This is still observed for the recently proposed Wasserstein GAN
formulation which stabilized adversarial training and allows considering high
capacity netwo... | computer science |
3,501 | Unsupervised Domain Adaptation with Copula Models | cs.LG | We study the task of unsupervised domain adaptation, where no labeled data
from the target domain is provided during training time. To deal with the
potential discrepancy between the source and target distributions, both in
features and labels, we exploit a copula-based regression framework. The
benefits of this approa... | computer science |
3,502 | Facial Key Points Detection using Deep Convolutional Neural Network -
NaimishNet | cs.CV | Facial Key Points (FKPs) Detection is an important and challenging problem in
the fields of computer vision and machine learning. It involves predicting the
co-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face.
In this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to
operate o... | computer science |
3,503 | A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds | cs.CV | When classifying point clouds, a large amount of time is devoted to the
process of engineering a reliable set of features which are then passed to a
classifier of choice. Generally, such features - usually derived from the
3D-covariance matrix - are computed using the surrounding neighborhood of
points. While these fea... | computer science |
3,504 | Efficient K-Shot Learning with Regularized Deep Networks | cs.CV | Feature representations from pre-trained deep neural networks have been known
to exhibit excellent generalization and utility across a variety of related
tasks. Fine-tuning is by far the simplest and most widely used approach that
seeks to exploit and adapt these feature representations to novel tasks with
limited data... | computer science |
3,505 | Real-Time Illegal Parking Detection System Based on Deep Learning | cs.CV | The increasing illegal parking has become more and more serious. Nowadays the
methods of detecting illegally parked vehicles are based on background
segmentation. However, this method is weakly robust and sensitive to
environment. Benefitting from deep learning, this paper proposes a novel
illegal vehicle parking detec... | computer science |
3,506 | Reconstruction of Hidden Representation for Robust Feature Extraction | cs.LG | This paper aims to develop a new and robust approach to feature
representation. Motivated by the success of Auto-Encoders, we first theoretical
summarize the general properties of all algorithms that are based on
traditional Auto-Encoders: 1) The reconstruction error of the input or
corrupted input can not be lower tha... | computer science |
3,507 | Self-Taught Support Vector Machine | cs.CV | In this paper, a new approach for classification of target task using limited
labeled target data as well as enormous unlabeled source data is proposed which
is called self-taught learning. The target and source data can be drawn from
different distributions. In the previous approaches, covariate shift assumption
is co... | computer science |
3,508 | The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? | cs.RO | A successful grasp requires careful balancing of the contact forces. Deducing
whether a particular grasp will be successful from indirect measurements, such
as vision, is therefore quite challenging, and direct sensing of contacts
through touch sensing provides an appealing avenue toward more successful and
consistent ... | computer science |
3,509 | TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization | cs.CV | Recent research implies that training and inference of deep neural networks
(DNN) can be computed with low precision numerical representations of the
training/test data, weights and gradients without a general loss in accuracy.
The benefit of such compact representations is twofold: they allow a
significant reduction o... | computer science |
3,510 | Learning Wasserstein Embeddings | stat.ML | The Wasserstein distance received a lot of attention recently in the
community of machine learning, especially for its principled way of comparing
distributions. It has found numerous applications in several hard problems,
such as domain adaptation, dimensionality reduction or generative models.
However, its use is sti... | computer science |
3,511 | Incomplete Dot Products for Dynamic Computation Scaling in Neural
Network Inference | cs.LG | We propose the use of incomplete dot products (IDP) to dynamically adjust the
number of input channels used in each layer of a convolutional neural network
during feedforward inference. IDP adds monotonically non-increasing
coefficients, referred to as a "profile", to the channels during training. The
profile orders th... | computer science |
3,512 | Rethinking Convolutional Semantic Segmentation Learning | cs.LG | Deep convolutional semantic segmentation (DCSS) learning doesn't converge to
an optimal local minimum with random parameters initializations; a pre-trained
model on the same domain becomes necessary to achieve convergence.In this work,
we propose a joint cooperative end-to-end learning method for DCSS. It
addresses man... | computer science |
3,513 | One pixel attack for fooling deep neural networks | cs.LG | Recent research has revealed that the output of Deep Neural Networks (DNN)
can be easily altered by adding relatively small perturbations to the input
vector. In this paper, we analyze an attack in an extremely limited scenario
where only one pixel can be modified. For that we propose a novel method for
generating one-... | computer science |
3,514 | Supervised Classification: Quite a Brief Overview | cs.LG | The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or ... | computer science |
3,515 | Stochastic Conjugate Gradient Algorithm with Variance Reduction | cs.LG | Conjugate gradient methods are a class of important methods for solving
linear equations and nonlinear optimization. In our work, we propose a new
stochastic conjugate gradient algorithm with variance reduction (CGVR) and
prove its linear convergence with the Fletcher and Revves method for strongly
convex and smooth fu... | computer science |
3,516 | Deep Learning for Accelerated Ultrasound Imaging | cs.CV | In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an
increasing demand to reconstruct high quality images from limited number of
data. However, the existing solutions require either hardware changes or
computationally expansive algorithms. To overcome these limitations, here we
propose a novel d... | computer science |
3,517 | Beyond Finite Layer Neural Networks: Bridging Deep Architectures and
Numerical Differential Equations | cs.CV | In our work, we bridge deep neural network design with numerical differential
equations. We show that many effective networks, such as ResNet, PolyNet,
FractalNet and RevNet, can be interpreted as different numerical
discretizations of differential equations. This finding brings us a brand new
perspective on the design... | computer science |
3,518 | Stochastic variance reduced multiplicative update for nonnegative matrix
factorization | cs.NA | Nonnegative matrix factorization (NMF), a dimensionality reduction and factor
analysis method, is a special case in which factor matrices have low-rank
nonnegative constraints. Considering the stochastic learning in NMF, we
specifically address the multiplicative update (MU) rule, which is the most
popular, but which h... | computer science |
3,519 | Denoising random forests | cs.CV | This paper proposes a novel type of random forests called a denoising random
forests that are robust against noises contained in test samples. Such
noise-corrupted samples cause serious damage to the estimation performances of
random forests, since unexpected child nodes are often selected and the leaf
nodes that the i... | computer science |
3,520 | A Connection between Feed-Forward Neural Networks and Probabilistic
Graphical Models | stat.ML | Two of the most popular modelling paradigms in computer vision are
feed-forward neural networks (FFNs) and probabilistic graphical models (GMs).
Various connections between the two have been studied in recent works, such as
e.g. expressing mean-field based inference in a GM as an FFN. This paper
establishes a new conne... | computer science |
3,521 | CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble
Behavior | cs.LG | We introduce a new deep convolutional neural network, CrescendoNet, by
stacking simple building blocks without residual connections. Each Crescendo
block contains independent convolution paths with increased depths. The numbers
of convolution layers and parameters are only increased linearly in Crescendo
blocks. In exp... | computer science |
3,522 | Accelerated Sparse Subspace Clustering | cs.LG | State-of-the-art algorithms for sparse subspace clustering perform spectral
clustering on a similarity matrix typically obtained by representing each data
point as a sparse combination of other points using either basis pursuit (BP)
or orthogonal matching pursuit (OMP). BP-based methods are often prohibitive in
practic... | computer science |
3,523 | Don't Decay the Learning Rate, Increase the Batch Size | cs.LG | It is common practice to decay the learning rate. Here we show one can
usually obtain the same learning curve on both training and test sets by
instead increasing the batch size during training. This procedure is successful
for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum,
and Adam. It reache... | computer science |
3,524 | Towards Reverse-Engineering Black-Box Neural Networks | stat.ML | Many deployed learned models are black boxes: given input, returns output.
Internal information about the model, such as the architecture, optimisation
procedure, or training data, is not disclosed explicitly as it might contain
proprietary information or make the system more vulnerable. This work shows
that such attri... | computer science |
3,525 | Optimal transport maps for distribution preserving operations on latent
spaces of Generative Models | cs.LG | Generative models such as Variational Auto Encoders (VAEs) and Generative
Adversarial Networks (GANs) are typically trained for a fixed prior
distribution in the latent space, such as uniform or Gaussian. After a trained
model is obtained, one can sample the Generator in various forms for
exploration and understanding,... | computer science |
3,526 | End-to-End Abnormality Detection in Medical Imaging | cs.CV | Nearly all of the deep learning based image analysis methods work on
reconstructed images, which are obtained from original acquisitions via solving
inverse problems. The reconstruction algorithms are designed for human
observers, but not necessarily optimized for DNNs. It is desirable to train the
DNNs directly from t... | computer science |
3,527 | Interpreting Convolutional Neural Networks Through Compression | stat.ML | Convolutional neural networks (CNNs) achieve state-of-the-art performance in
a wide variety of tasks in computer vision. However, interpreting CNNs still
remains a challenge. This is mainly due to the large number of parameters in
these networks. Here, we investigate the role of compression and particularly
pruning fil... | computer science |
3,528 | Moonshine: Distilling with Cheap Convolutions | stat.ML | Model distillation compresses a trained machine learning model, such as a
neural network, into a smaller alternative such that it could be easily
deployed in a resource limited setting. Unfortunately, this requires
engineering two architectures: a student architecture smaller than the first
teacher architecture but tra... | computer science |
3,529 | Deep Hyperspherical Learning | cs.LG | Convolution as inner product has been the founding basis of convolutional
neural networks (CNNs) and the key to end-to-end visual representation
learning. Benefiting from deeper architectures, recent CNNs have demonstrated
increasingly strong representation abilities. Despite such improvement, the
increased depth and l... | computer science |
3,530 | Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine
Learning | stat.ML | Obtaining detailed and reliable data about local economic livelihoods in
developing countries is expensive, and data are consequently scarce. Previous
work has shown that it is possible to measure local-level economic livelihoods
using high-resolution satellite imagery. However, such imagery is relatively
expensive to ... | computer science |
3,531 | Breast density classification with deep convolutional neural networks | cs.CV | Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the... | computer science |
3,532 | CUR Decompositions, Similarity Matrices, and Subspace Clustering | cs.LG | A general framework for solving the subspace clustering problem using the CUR
decomposition is presented. The CUR decomposition provides a natural way to
construct similarity matrices for data that come from a union of unknown
subspaces $\mathscr{U}=\underset{i=1}{\overset{M}\bigcup}S_i$. The similarity
matrices thus c... | computer science |
3,533 | Learning and Visualizing Localized Geometric Features Using 3D-CNN: An
Application to Manufacturability Analysis of Drilled Holes | stat.ML | 3D Convolutional Neural Networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. However, interpreting
the decision making process of these 3D-CNNs is still an infeasible task. In
this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
Mapping method (3D... | computer science |
3,534 | Modeling Human Categorization of Natural Images Using Deep Feature
Representations | cs.CV | Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visu... | computer science |
3,535 | CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep
Learning | cs.CV | We develop an algorithm that can detect pneumonia from chest X-rays at a
level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer
convolutional neural network trained on ChestX-ray14, currently the largest
publicly available chest X-ray dataset, containing over 100,000 frontal-view
X-ray images w... | computer science |
3,536 | Sliced Wasserstein Distance for Learning Gaussian Mixture Models | cs.CV | Gaussian mixture models (GMM) are powerful parametric tools with many
applications in machine learning and computer vision. Expectation maximization
(EM) is the most popular algorithm for estimating the GMM parameters. However,
EM guarantees only convergence to a stationary point of the log-likelihood
function, which c... | computer science |
3,537 | MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis | stat.ML | Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN,... | computer science |
3,538 | Aggregated Wasserstein Metric and State Registration for Hidden Markov
Models | cs.LG | We propose a framework, named Aggregated Wasserstein, for computing a
dissimilarity measure or distance between two Hidden Markov Models with state
conditional distributions being Gaussian. For such HMMs, the marginal
distribution at any time position follows a Gaussian mixture distribution, a
fact exploited to softly ... | computer science |
3,539 | A Forward-Backward Approach for Visualizing Information Flow in Deep
Networks | stat.ML | We introduce a new, systematic framework for visualizing information flow in
deep networks. Specifically, given any trained deep convolutional network model
and a given test image, our method produces a compact support in the image
domain that corresponds to a (high-resolution) feature that contributes to the
given exp... | computer science |
3,540 | BPGrad: Towards Global Optimality in Deep Learning via Branch and
Pruning | stat.ML | Understanding the global optimality in deep learning (DL) has been attracting
more and more attention recently. Conventional DL solvers, however, have not
been developed intentionally to seek for such global optimality. In this paper
we propose a novel approximation algorithm, BPGrad, towards optimizing deep
models glo... | computer science |
3,541 | Unsupervised Domain Adaptation for Semantic Segmentation with GANs | cs.CV | Visual Domain Adaptation is a problem of immense importance in computer
vision. Previous approaches showcase the inability of even deep neural networks
to learn informative representations across domain shift. This problem is more
severe for tasks where acquiring hand labeled data is extremely hard and
tedious. In this... | computer science |
3,542 | Convergent Block Coordinate Descent for Training Tikhonov Regularized
Deep Neural Networks | stat.ML | By lifting the ReLU function into a higher dimensional space, we develop a
smooth multi-convex formulation for training feed-forward deep neural networks
(DNNs). This allows us to develop a block coordinate descent (BCD) training
algorithm consisting of a sequence of numerically well-behaved convex
optimizations. Using... | computer science |
3,543 | Virtual Adversarial Ladder Networks For Semi-supervised Learning | cs.LG | Semi-supervised learning (SSL) partially circumvents the high cost of
labeling data by augmenting a small labeled dataset with a large and relatively
cheap unlabeled dataset drawn from the same distribution. This paper offers a
novel interpretation of two deep learning-based SSL approaches, ladder networks
and virtual ... | computer science |
3,544 | Autoencoder Node Saliency: Selecting Relevant Latent Representations | cs.CV | The autoencoder is an artificial neural network model that learns hidden
representations of unlabeled data. With a linear transfer function it is
similar to the principal component analysis (PCA). While both methods use
weight vectors for linear transformations, the autoencoder does not come with
any indication similar... | computer science |
3,545 | The Riemannian Geometry of Deep Generative Models | cs.LG | Deep generative models learn a mapping from a low dimensional latent space to
a high-dimensional data space. Under certain regularity conditions, these
models parameterize nonlinear manifolds in the data space. In this paper, we
investigate the Riemannian geometry of these generated manifolds. First, we
develop efficie... | computer science |
3,546 | Few-shot Learning by Exploiting Visual Concepts within CNNs | cs.CV | Convolutional neural networks (CNNs) are one of the driving forces for the
advancement of computer vision. Despite their promising performances on many
tasks, CNNs still face major obstacles on the road to achieving ideal machine
intelligence. One is that CNNs are complex and hard to interpret. Another is
that standard... | computer science |
3,547 | Stacked Kernel Network | stat.ML | Kernel methods are powerful tools to capture nonlinear patterns behind data.
They implicitly learn high (even infinite) dimensional nonlinear features in
the Reproducing Kernel Hilbert Space (RKHS) while making the computation
tractable by leveraging the kernel trick. Classic kernel methods learn a single
layer of nonl... | computer science |
3,548 | An Introduction to Deep Visual Explanation | stat.ML | The practical impact of deep learning on complex supervised learning problems
has been significant, so much so that almost every Artificial Intelligence
problem, or at least a portion thereof, has been somehow recast as a deep
learning problem. The applications appeal is significant, but this appeal is
increasingly cha... | computer science |
3,549 | Deformation estimation of an elastic object by partial observation using
a neural network | cs.CV | Deformation estimation of elastic object assuming an internal organ is
important for the computer navigation of surgery. The aim of this study is to
estimate the deformation of an entire three-dimensional elastic object using
displacement information of very few observation points. A learning approach
with a neural net... | computer science |
3,550 | Between-class Learning for Image Classification | cs.LG | In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC ... | computer science |
3,551 | Paris-Lille-3D: a large and high-quality ground truth urban point cloud
dataset for automatic segmentation and classification | cs.LG | This paper introduces a new Urban Point Cloud Dataset for Automatic
Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
describe how the dataset is obtained from acquisition to post-processing and
labeling. This dataset can be used to learn classification algorithm, however,
given that a great a... | computer science |
3,552 | Progressive Neural Architecture Search | cs.CV | We propose a method for learning CNN structures that is more efficient than
previous approaches: instead of using reinforcement learning (RL) or genetic
algorithms (GA), we use a sequential model-based optimization (SMBO) strategy,
in which we search for architectures in order of increasing complexity, while
simultaneo... | computer science |
3,553 | Data Dropout in Arbitrary Basis for Deep Network Regularization | cs.CV | An important problem in training deep networks with high capacity is to
ensure that the trained network works well when presented with new inputs
outside the training dataset. Dropout is an effective regularization technique
to boost the network generalization in which a random subset of the elements of
the given data ... | computer science |
3,554 | OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for
Deep Learning | cs.CV | Deep neural networks trained using a softmax layer at the top and the
cross-entropy loss are ubiquitous tools for image classification. Yet, this
does not naturally enforce intra-class similarity nor inter-class margin of the
learned deep representations. To simultaneously achieve these two goals,
different solutions h... | computer science |
3,555 | Deep Gradient Compression: Reducing the Communication Bandwidth for
Distributed Training | cs.CV | Large-scale distributed training requires significant communication bandwidth
for gradient exchange that limits the scalability of multi-node training, and
requires expensive high-bandwidth network infrastructure. The situation gets
even worse with distributed training on mobile devices (federated learning),
which suff... | computer science |
3,556 | AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks | cs.LG | Training deep neural networks with Stochastic Gradient Descent, or its
variants, requires careful choice of both learning rate and batch size. While
smaller batch sizes generally converge in fewer training epochs, larger batch
sizes offer more parallelism and hence better computational efficiency. We have
developed a n... | computer science |
3,557 | Guided Labeling using Convolutional Neural Networks | cs.CV | Over the last couple of years, deep learning and especially convolutional
neural networks have become one of the work horses of computer vision. One
limiting factor for the applicability of supervised deep learning to more areas
is the need for large, manually labeled datasets. In this paper we propose an
easy to imple... | computer science |
3,558 | Learning Random Fourier Features by Hybrid Constrained Optimization | stat.ML | The kernel embedding algorithm is an important component for adapting kernel
methods to large datasets. Since the algorithm consumes a major computation
cost in the testing phase, we propose a novel teacher-learner framework of
learning computation-efficient kernel embeddings from specific data. In the
framework, the h... | computer science |
3,559 | Solving internal covariate shift in deep learning with linked neurons | stat.ML | This work proposes a novel solution to the problem of internal covariate
shift and dying neurons using the concept of linked neurons. We define the
neuron linkage in terms of two constraints: first, all neuron activations in
the linkage must have the same operating point. That is to say, all of them
share input weights... | computer science |
3,560 | End-to-end Learning of Deterministic Decision Trees | stat.ML | Conventional decision trees have a number of favorable properties, including
interpretability, a small computational footprint and the ability to learn from
little training data. However, they lack a key quality that has helped fuel the
deep learning revolution: that of being end-to-end trainable, and to learn from
scr... | computer science |
3,561 | CycleGAN, a Master of Steganography | cs.CV | CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a
transformation between two image distributions. In a series of experiments, we
demonstrate an intriguing property of the model: CycleGAN learns to "hide"
information about a source image into the images it generates in a nearly
imperceptible, high-... | computer science |
3,562 | Bayesian Joint Matrix Decomposition for Data Integration with
Heterogeneous Noise | cs.CV | Matrix decomposition is a popular and fundamental approach in machine
learning and data mining. It has been successfully applied into various fields.
Most matrix decomposition methods focus on decomposing a data matrix from one
single source. However, it is common that data are from different sources with
heterogeneous... | computer science |
3,563 | Generalized Zero-Shot Learning via Synthesized Examples | cs.LG | We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/... | computer science |
3,564 | Eye In-Painting with Exemplar Generative Adversarial Networks | cs.CV | This paper introduces a novel approach to in-painting where the identity of
the object to remove or change is preserved and accounted for at inference
time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize
exemplar information to produce high-quality, personalized in painting results.
We propos... | computer science |
3,565 | Logo Synthesis and Manipulation with Clustered Generative Adversarial
Networks | cs.CV | Designing a logo for a new brand is a lengthy and tedious back-and-forth
process between a designer and a client. In this paper we explore to what
extent machine learning can solve the creative task of the designer. For this,
we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web.
Training Generati... | computer science |
3,566 | Deep Burst Denoising | cs.CV | Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease n... | computer science |
3,567 | Visual Explanation by Interpretation: Improving Visual Feedback
Capabilities of Deep Neural Networks | cs.CV | Learning-based representations have become the defacto means to address
computer vision tasks. Despite their massive adoption, the amount of work
aiming at understanding the internal representations learned by these models is
rather limited. Existing methods aimed at model interpretation either require
exhaustive manua... | computer science |
3,568 | On the Effectiveness of Least Squares Generative Adversarial Networks | cs.CV | Unsupervised learning with generative adversarial networks (GANs) has proven
hugely successful. Regular GANs hypothesize the discriminator as a classifier
with the sigmoid cross entropy loss function. However, we found that this loss
function may lead to the vanishing gradients problem during the learning
process. To o... | computer science |
3,569 | Learning to Write Stylized Chinese Characters by Reading a Handful of
Examples | cs.CV | Automatically writing stylized Chinese characters is an attractive yet
challenging task due to its wide applicabilities. In this paper, we propose a
novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
generate Chinese characters. Specifically, we propose to capture the different
characterist... | computer science |
3,570 | Adversarial Examples: Attacks and Defenses for Deep Learning | cs.LG | With rapid progress and great successes in a wide spectrum of applications,
deep learning is being applied in many safety-critical environments. However,
deep neural networks have been recently found vulnerable to well-designed input
samples, called \textit{adversarial examples}. Adversarial examples are
imperceptible ... | computer science |
3,571 | Query-Efficient Black-box Adversarial Examples | cs.CV | Current neural network-based image classifiers are susceptible to adversarial
examples, even in the black-box setting, where the attacker is limited to query
access without access to gradients. Previous methods --- substitute networks
and coordinate-based finite-difference methods --- are either unreliable or
query-ine... | computer science |
3,572 | Hyperparameters Optimization in Deep Convolutional Neural Network /
Bayesian Approach with Gaussian Process Prior | cs.CV | Convolutional Neural Network is known as ConvNet have been extensively used
in many complex machine learning tasks. However, hyperparameters optimization
is one of a crucial step in developing ConvNet architectures, since the
accuracy and performance are reliant on the hyperparameters. This multilayered
architecture pa... | computer science |
3,573 | Finding Competitive Network Architectures Within a Day Using UCT | cs.LG | The design of neural network architectures for a new data set is a laborious
task which requires human deep learning expertise. In order to make deep
learning available for a broader audience, automated methods for finding a
neural network architecture are vital. Recently proposed methods can already
achieve human expe... | computer science |
3,574 | Image Segmentation to Distinguish Between Overlapping Human Chromosomes | cs.CV | In medicine, visualizing chromosomes is important for medical diagnostics,
drug development, and biomedical research. Unfortunately, chromosomes often
overlap and it is necessary to identify and distinguish between the overlapping
chromosomes. A segmentation solution that is fast and automated will enable
scaling of co... | computer science |
3,575 | A Deep Learning Interpretable Classifier for Diabetic Retinopathy
Disease Grading | cs.LG | Deep neural network models have been proven to be very successful in image
classification tasks, also for medical diagnosis, but their main concern is its
lack of interpretability. They use to work as intuition machines with high
statistical confidence but unable to give interpretable explanations about the
reported re... | computer science |
3,576 | The Robust Manifold Defense: Adversarial Training using Generative
Models | cs.CV | Deep neural networks are demonstrating excellent performance on several
classical vision problems. However, these networks are vulnerable to
adversarial examples, minutely modified images that induce arbitrary
attacker-chosen output from the network. We propose a mechanism to protect
against these adversarial inputs ba... | computer science |
3,577 | Extrapolating Expected Accuracies for Large Multi-Class Problems | stat.ML | The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumptions that the classes are sampled identically and independently from a
population, a... | computer science |
3,578 | Visualizing the Loss Landscape of Neural Nets | cs.LG | Neural network training relies on our ability to find "good" minimizers of
highly non-convex loss functions. It is well known that certain network
architecture designs (e.g., skip connections) produce loss functions that train
easier, and well-chosen training parameters (batch size, learning rate,
optimizer) produce mi... | computer science |
3,579 | Optimal Bayesian Transfer Learning | stat.ML | Transfer learning has recently attracted significant research attention, as
it simultaneously learns from different source domains, which have plenty of
labeled data, and transfers the relevant knowledge to the target domain with
limited labeled data to improve the prediction performance. We propose a
Bayesian transfer... | computer science |
3,580 | Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space | cs.LG | Recently, Neural networks have seen a huge surge in its adoption due to their
ability to provide high accuracy on various tasks. On the other hand, the
existence of adversarial examples have raised suspicions regarding the
generalization capabilities of neural networks. In this work, we focus on the
weight matrix learn... | computer science |
3,581 | Graph Autoencoder-Based Unsupervised Feature Selection with Broad and
Local Data Structure Preservation | cs.CV | Feature selection is a dimensionality reduction technique that selects a
subset of representative features from high-dimensional data by eliminating
irrelevant and redundant features. Recently, feature selection combined with
sparse learning has attracted significant attention due to its outstanding
performance compare... | computer science |
3,582 | Anatomical Data Augmentation For CNN based Pixel-wise Classification | cs.CV | In this work we propose a method for anatomical data augmentation that is
based on using slices of computed tomography (CT) examinations that are
adjacent to labeled slices as another resource of labeled data for training the
network. The extended labeled data is used to train a U-net network for a
pixel-wise classific... | computer science |
3,583 | Boundary Optimizing Network (BON) | cs.LG | Despite all the success that deep neural networks have seen in classifying
certain datasets, the challenge of finding optimal solutions that generalize
still remains. In this paper, we propose the Boundary Optimizing Network (BON),
a new approach to generalization for deep neural networks when used for
supervised learn... | computer science |
3,584 | Data Augmentation by Pairing Samples for Images Classification | cs.LG | Data augmentation is a widely used technique in many machine learning tasks,
such as image classification, to virtually enlarge the training dataset size
and avoid overfitting. Traditional data augmentation techniques for image
classification tasks create new samples from the original training data by, for
example, fli... | computer science |
3,585 | Fix your classifier: the marginal value of training the last weight
layer | cs.LG | Neural networks are commonly used as models for classification for a wide
variety of tasks. Typically, a learned affine transformation is placed at the
end of such models, yielding a per-class value used for classification. This
classifier can have a vast number of parameters, which grows linearly with the
number of po... | computer science |
3,586 | Generalizing, Decoding, and Optimizing Support Vector Machine
Classification | cs.LG | The classification of complex data usually requires the composition of
processing steps. Here, a major challenge is the selection of optimal
algorithms for preprocessing and classification (including parameterizations).
Nowadays, parts of the optimization process are automized but expert knowledge
and manual work are s... | computer science |
3,587 | Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace | stat.ML | Gradient-based meta-learning has been shown to be expressive enough to
approximate any learning algorithm. While previous such methods have been
successful in meta-learning tasks, they resort to simple gradient descent
during meta-testing. Our primary contribution is the {\em MT-net}, which
enables the meta-learner to ... | computer science |
3,588 | Brenier approach for optimal transportation between a quasi-discrete
measure and a discrete measure | cs.CV | Correctly estimating the discrepancy between two data distributions has
always been an important task in Machine Learning. Recently, Cuturi proposed
the Sinkhorn distance which makes use of an approximate Optimal Transport cost
between two distributions as a distance to describe distribution discrepancy.
Although it ha... | computer science |
3,589 | Robust Kronecker Component Analysis | stat.ML | Dictionary learning and component analysis models are fundamental in learning
compact representations that are relevant to a given task (feature extraction,
dimensionality reduction, denoising, etc.). The model complexity is encoded by
means of specific structure, such as sparsity, low-rankness, or nonnegativity.
Unfor... | computer science |
3,590 | Worst-case Optimal Submodular Extensions for Marginal Estimation | cs.LG | Submodular extensions of an energy function can be used to efficiently
compute approximate marginals via variational inference. The accuracy of the
marginals depends crucially on the quality of the submodular extension. To
identify the best possible extension, we show an equivalence between the
submodular extensions of... | computer science |
3,591 | Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate
Modeling and Uncertainty Quantification | cs.CV | We are interested in the development of surrogate models for uncertainty
quantification and propagation in problems governed by stochastic PDEs using a
deep convolutional encoder-decoder network in a similar fashion to approaches
considered in deep learning for image-to-image regression tasks. Since normal
neural netwo... | computer science |
3,592 | E-swish: Adjusting Activations to Different Network Depths | cs.CV | Activation functions have a notorious impact on neural networks on both
training and testing the models against the desired problem. Currently, the
most used activation function is the Rectified Linear Unit (ReLU). This paper
introduces a new and novel activation function, closely related with the new
activation $Swish... | computer science |
3,593 | Deep Learning with Data Dependent Implicit Activation Function | cs.LG | Though deep neural networks (DNNs) achieve remarkable performances in many
artificial intelligence tasks, the lack of training instances remains a
notorious challenge. As the network goes deeper, the generalization accuracy
decays rapidly in the situation of lacking massive amounts of training data. In
this paper, we p... | computer science |
3,594 | AFT*: Integrating Active Learning and Transfer Learning to Reduce
Annotation Efforts | cs.LG | The splendid success of convolutional neural networks (CNNs) in computer
vision is largely attributed to the availability of large annotated datasets,
such as ImageNet and Places. However, in biomedical imaging, it is very
challenging to create such large annotated datasets, as annotating biomedical
images is not only ... | computer science |
3,595 | Enhancing Multi-Class Classification of Random Forest using Random
Vector Functional Neural Network and Oblique Decision Surfaces | cs.LG | Both neural networks and decision trees are popular machine learning methods
and are widely used to solve problems from diverse domains. These two
classifiers are commonly used base classifiers in an ensemble framework. In
this paper, we first present a new variant of oblique decision tree based on a
linear classifier,... | computer science |
3,596 | Adversarial Vulnerability of Neural Networks Increases With Input
Dimension | stat.ML | Over the past four years, neural networks have proven vulnerable to
adversarial images: targeted but imperceptible image perturbations lead to
drastically different predictions. We show that adversarial vulnerability
increases with the gradients of the training objective when seen as a function
of the inputs. For most ... | computer science |
3,597 | DeepTravel: a Neural Network Based Travel Time Estimation Model with
Auxiliary Supervision | cs.LG | Estimating the travel time of a path is of great importance to smart urban
mobility. Existing approaches are either based on estimating the time cost of
each road segment which are not able to capture many cross-segment complex
factors, or designed heuristically in a non-learning-based way which fail to
utilize the exi... | computer science |
3,598 | Classification and Disease Localization in Histopathology Using Only
Global Labels: A Weakly-Supervised Approach | cs.CV | Analysis of histopathology slides is a critical step for many diagnoses, and
in particular in oncology where it defines the gold standard. In the case of
digital histopathological analysis, highly trained pathologists must review
vast whole-slide-images of extreme digital resolution ($100,000^2$ pixels)
across multiple... | computer science |
3,599 | Spectral Image Visualization Using Generative Adversarial Networks | cs.CV | Spectral images captured by satellites and radio-telescopes are analyzed to
obtain information about geological compositions distributions, distant asters
as well as undersea terrain. Spectral images usually contain tens to hundreds
of continuous narrow spectral bands and are widely used in various fields. But
the vast... | computer science |
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