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3,400 | Grad-CAM: Why did you say that? | stat.ML | We propose a technique for making Convolutional Neural Network (CNN)-based
models more transparent by visualizing input regions that are 'important' for
predictions -- or visual explanations. Our approach, called Gradient-weighted
Class Activation Mapping (Grad-CAM), uses class-specific gradient information
to localize... | computer science |
3,401 | Inducing Interpretable Representations with Variational Autoencoders | stat.ML | We develop a framework for incorporating structured graphical models in the
\emph{encoders} of variational autoencoders (VAEs) that allows us to induce
interpretable representations through approximate variational inference. This
allows us to both perform reasoning (e.g. classification) under the structural
constraints... | computer science |
3,402 | iCaRL: Incremental Classifier and Representation Learning | cs.CV | A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training dat... | computer science |
3,403 | Gossip training for deep learning | cs.CV | We address the issue of speeding up the training of convolutional networks.
Here we study a distributed method adapted to stochastic gradient descent
(SGD). The parallel optimization setup uses several threads, each applying
individual gradient descents on a local variable. We propose a new way to share
information bet... | computer science |
3,404 | Machine Learning for Dental Image Analysis | stat.ML | In order to study the application of artificial intelligence (AI) to dental
imaging, we applied AI technology to classify a set of panoramic radiographs
using (a) a convolutional neural network (CNN) which is a form of an artificial
neural network (ANN), (b) representative image cognition algorithms that
implement scal... | computer science |
3,405 | Active Deep Learning for Classification of Hyperspectral Images | cs.LG | Active deep learning classification of hyperspectral images is considered in
this paper. Deep learning has achieved success in many applications, but
good-quality labeled samples are needed to construct a deep learning network.
It is expensive getting good labeled samples in hyperspectral images for remote
sensing appl... | computer science |
3,406 | Stochastic Generative Hashing | cs.LG | Learning-based binary hashing has become a powerful paradigm for fast search
and retrieval in massive databases. However, due to the requirement of discrete
outputs for the hash functions, learning such functions is known to be very
challenging. In addition, the objective functions adopted by existing hashing
technique... | computer science |
3,407 | Multivariate Regression with Grossly Corrupted Observations: A Robust
Approach and its Applications | stat.ML | This paper studies the problem of multivariate linear regression where a
portion of the observations is grossly corrupted or is missing, and the
magnitudes and locations of such occurrences are unknown in priori. To deal
with this problem, we propose a new approach by explicitly consider the error
source as well as its... | computer science |
3,408 | A More General Robust Loss Function | cs.CV | We present a two-parameter loss function which can be viewed as a
generalization of many popular loss functions used in robust statistics: the
Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, and generalized Charbonnier
loss functions (and by transitivity the L2, L1, L1-L2, and
pseudo-Huber/Charbonnier loss functions)... | computer science |
3,409 | Learning what to look in chest X-rays with a recurrent visual attention
model | stat.ML | X-rays are commonly performed imaging tests that use small amounts of
radiation to produce pictures of the organs, tissues, and bones of the body.
X-rays of the chest are used to detect abnormalities or diseases of the
airways, blood vessels, bones, heart, and lungs. In this work we present a
stochastic attention-based... | computer science |
3,410 | Self-Adaptation of Activity Recognition Systems to New Sensors | cs.CV | Traditional activity recognition systems work on the basis of training,
taking a fixed set of sensors into account. In this article, we focus on the
question how pattern recognition can leverage new information sources without
any, or with minimal user input. Thus, we present an approach for opportunistic
activity reco... | computer science |
3,411 | Towards Adversarial Retinal Image Synthesis | cs.CV | Synthesizing images of the eye fundus is a challenging task that has been
previously approached by formulating complex models of the anatomy of the eye.
New images can then be generated by sampling a suitable parameter space. In
this work, we propose a method that learns to synthesize eye fundus images
directly from da... | computer science |
3,412 | Low Rank Matrix Recovery with Simultaneous Presence of Outliers and
Sparse Corruption | stat.ML | We study a data model in which the data matrix D can be expressed as D = L +
S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
matrix whose non-zero columns are outlying data points. To date, robust PCA
algorithms have solely considered models with either S or C, but not both. As
such, existi... | computer science |
3,413 | Learning Deep Features via Congenerous Cosine Loss for Person
Recognition | cs.CV | Person recognition aims at recognizing the same identity across time and
space with complicated scenes and similar appearance. In this paper, we propose
a novel method to address this task by training a network to obtain robust and
representative features. The intuition is that we directly compare and optimize
the cosi... | computer science |
3,414 | An EM Based Probabilistic Two-Dimensional CCA with Application to Face
Recognition | cs.CV | Recently, two-dimensional canonical correlation analysis (2DCCA) has been
successfully applied for image feature extraction. The method instead of
concatenating the columns of the images to the one-dimensional vectors,
directly works with two-dimensional image matrices. Although 2DCCA works well
in different recognitio... | computer science |
3,415 | Supervised Learning of Labeled Pointcloud Differences via Cover-Tree
Entropy Reduction | cs.LG | We introduce a new algorithm, called CDER, for supervised machine learning
that merges the multi-scale geometric properties of Cover Trees with the
information-theoretic properties of entropy. CDER applies to a training set of
labeled pointclouds embedded in a common Euclidean space. If typical
pointclouds correspondin... | computer science |
3,416 | Fast and Accurate Inference with Adaptive Ensemble Prediction in Image
Classification with Deep Neural Networks | cs.LG | Ensembling multiple predictions is a widely used technique to improve the
accuracy of various machine learning tasks. In image classification tasks, for
example, averaging the predictions for multiple patches extracted from the
input image significantly improves accuracy. Using multiple networks trained
independently t... | computer science |
3,417 | Theoretical Properties for Neural Networks with Weight Matrices of Low
Displacement Rank | cs.LG | Recently low displacement rank (LDR) matrices, or so-called structured
matrices, have been proposed to compress large-scale neural networks. Empirical
results have shown that neural networks with weight matrices of LDR matrices,
referred as LDR neural networks, can achieve significant reduction in space and
computation... | computer science |
3,418 | Binarized Convolutional Landmark Localizers for Human Pose Estimation
and Face Alignment with Limited Resources | cs.CV | Our goal is to design architectures that retain the groundbreaking
performance of CNNs for landmark localization and at the same time are
lightweight, compact and suitable for applications with limited computational
resources. To this end, we make the following contributions: (a) we are the
first to study the effect of... | computer science |
3,419 | Using Synthetic Data to Train Neural Networks is Model-Based Reasoning | cs.LG | We draw a formal connection between using synthetic training data to optimize
neural network parameters and approximate, Bayesian, model-based reasoning. In
particular, training a neural network using synthetic data can be viewed as
learning a proposal distribution generator for approximate inference in the
synthetic-d... | computer science |
3,420 | Belief Propagation in Conditional RBMs for Structured Prediction | cs.LG | Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular
models for a wide range of applications. In previous work, learning on such
models has been dominated by contrastive divergence~(CD) and its variants.
Belief propagation~(BP) algorithms are believed to be slow for structured
prediction on con... | computer science |
3,421 | Denoising Adversarial Autoencoders | cs.CV | Unsupervised learning is of growing interest because it unlocks the potential
held in vast amounts of unlabelled data to learn useful representations for
inference. Autoencoders, a form of generative model, may be trained by learning
to reconstruct unlabelled input data from a latent representation space. More
robust r... | computer science |
3,422 | Learning from Noisy Labels with Distillation | cs.CV | The ability of learning from noisy labels is very useful in many visual
recognition tasks, as a vast amount of data with noisy labels are relatively
easy to obtain. Traditionally, the label noises have been treated as
statistical outliers, and approaches such as importance re-weighting and
bootstrap have been proposed ... | computer science |
3,423 | Deep Bayesian Active Learning with Image Data | cs.LG | Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of ... | computer science |
3,424 | Discriminate-and-Rectify Encoders: Learning from Image Transformation
Sets | cs.CV | The complexity of a learning task is increased by transformations in the
input space that preserve class identity. Visual object recognition for example
is affected by changes in viewpoint, scale, illumination or planar
transformations. While drastically altering the visual appearance, these
changes are orthogonal to r... | computer science |
3,425 | Neural Networks for Beginners. A fast implementation in Matlab, Torch,
TensorFlow | cs.LG | This report provides an introduction to some Machine Learning tools within
the most common development environments. It mainly focuses on practical
problems, skipping any theoretical introduction. It is oriented to both
students trying to approach Machine Learning and experts looking for new
frameworks. | computer science |
3,426 | On the Limitation of Convolutional Neural Networks in Recognizing
Negative Images | cs.CV | Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics... | computer science |
3,427 | Cross-modal Deep Metric Learning with Multi-task Regularization | cs.LG | DNN-based cross-modal retrieval has become a research hotspot, by which users
can search results across various modalities like image and text. However,
existing methods mainly focus on the pairwise correlation and reconstruction
error of labeled data. They ignore the semantically similar and dissimilar
constraints bet... | computer science |
3,428 | High-Resolution Breast Cancer Screening with Multi-View Deep
Convolutional Neural Networks | cs.CV | Recent advances in deep learning for natural images has prompted a surge of
interest in applying similar techniques to medical images. Most of the initial
attempts focused on replacing the input of a deep convolutional neural network
with a medical image, which does not take into consideration the fundamental
differenc... | computer science |
3,429 | Knowledge distillation using unlabeled mismatched images | cs.CV | Current approaches for Knowledge Distillation (KD) either directly use
training data or sample from the training data distribution. In this paper, we
demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for
image classification networks. For illustration, we consider scenarios where
this is a comp... | computer science |
3,430 | Episode-Based Active Learning with Bayesian Neural Networks | cs.CV | We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final trai... | computer science |
3,431 | Count-ception: Counting by Fully Convolutional Redundant Counting | cs.CV | Counting objects in digital images is a process that should be replaced by
machines. This tedious task is time consuming and prone to errors due to
fatigue of human annotators. The goal is to have a system that takes as input
an image and returns a count of the objects inside and justification for the
prediction in the... | computer science |
3,432 | Convolutional Neural Networks for Page Segmentation of Historical
Document Images | cs.CV | This paper presents a Convolutional Neural Network (CNN) based page
segmentation method for handwritten historical document images. We consider
page segmentation as a pixel labeling problem, i.e., each pixel is classified
as one of the predefined classes. Traditional methods in this area rely on
carefully hand-crafted ... | computer science |
3,433 | The Relative Performance of Ensemble Methods with Deep Convolutional
Neural Networks for Image Classification | stat.ML | Artificial neural networks have been successfully applied to a variety of
machine learning tasks, including image recognition, semantic segmentation, and
machine translation. However, few studies fully investigated ensembles of
artificial neural networks. In this work, we investigated multiple widely used
ensemble meth... | computer science |
3,434 | It Takes (Only) Two: Adversarial Generator-Encoder Networks | cs.CV | We present a new autoencoder-type architecture that is trainable in an
unsupervised mode, sustains both generation and inference, and has the quality
of conditional and unconditional samples boosted by adversarial learning.
Unlike previous hybrids of autoencoders and adversarial networks, the
adversarial game in our ap... | computer science |
3,435 | CERN: Confidence-Energy Recurrent Network for Group Activity Recognition | cs.CV | This work is about recognizing human activities occurring in videos at
distinct semantic levels, including individual actions, interactions, and group
activities. The recognition is realized using a two-level hierarchy of Long
Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture,
which can be tra... | computer science |
3,436 | Deep-FExt: Deep Feature Extraction for Vessel Segmentation and
Centerline Prediction | stat.ML | Feature extraction is a very crucial task in image and pixel (voxel)
classification and regression in biomedical image modeling. In this work we
present a machine learning based feature extraction scheme based on inception
models for pixel classification tasks. We extract features under multi-scale
and multi-layer sche... | computer science |
3,437 | On the Effects of Batch and Weight Normalization in Generative
Adversarial Networks | stat.ML | Generative adversarial networks (GANs) are highly effective unsupervised
learning frameworks that can generate very sharp data, even for data such as
images with complex, highly multimodal distributions. However GANs are known to
be very hard to train, suffering from problems such as mode collapse and
disturbing visual... | computer science |
3,438 | Close Yet Distinctive Domain Adaptation | cs.LG | Domain adaptation is transfer learning which aims to generalize a learning
model across training and testing data with different distributions. Most
previous research tackle this problem in seeking a shared feature
representation between source and target domains while reducing the mismatch of
their data distributions.... | computer science |
3,439 | Unsupervised Learning by Predicting Noise | stat.ML | Convolutional neural networks provide visual features that perform remarkably
well in many computer vision applications. However, training these networks
requires significant amounts of supervision. This paper introduces a generic
framework to train deep networks, end-to-end, with no supervision. We propose
to fix a se... | computer science |
3,440 | Ranking to Learn: Feature Ranking and Selection via Eigenvector
Centrality | cs.CV | In an era where accumulating data is easy and storing it inexpensive, feature
selection plays a central role in helping to reduce the high-dimensionality of
huge amounts of otherwise meaningless data. In this paper, we propose a
graph-based method for feature selection that ranks features by identifying the
most import... | computer science |
3,441 | Fast Generation for Convolutional Autoregressive Models | cs.LG | Convolutional autoregressive models have recently demonstrated
state-of-the-art performance on a number of generation tasks. While fast,
parallel training methods have been crucial for their success, generation is
typically implemented in a na\"{i}ve fashion where redundant computations are
unnecessarily repeated. This... | computer science |
3,442 | Segmentation of the Proximal Femur from MR Images using Deep
Convolutional Neural Networks | cs.CV | Magnetic resonance imaging (MRI) has been proposed as a complimentary method
to measure bone quality and assess fracture risk. However, manual segmentation
of MR images of bone is time-consuming, limiting the use of MRI measurements in
the clinical practice. The purpose of this paper is to present an automatic
proximal... | computer science |
3,443 | Robust, Deep and Inductive Anomaly Detection | cs.LG | PCA is a classical statistical technique whose simplicity and maturity has
seen it find widespread use as an anomaly detection technique. However, it is
limited in this regard by being sensitive to gross perturbations of the input,
and by seeking a linear subspace that captures normal behaviour. The first
issue has bee... | computer science |
3,444 | Twin Learning for Similarity and Clustering: A Unified Kernel Approach | cs.LG | Many similarity-based clustering methods work in two separate steps including
similarity matrix computation and subsequent spectral clustering. However,
similarity measurement is challenging because it is usually impacted by many
factors, e.g., the choice of similarity metric, neighborhood size, scale of
data, noise an... | computer science |
3,445 | Detecting Adversarial Samples Using Density Ratio Estimates | cs.LG | Machine learning models, especially based on deep architectures are used in
everyday applications ranging from self driving cars to medical diagnostics. It
has been shown that such models are dangerously susceptible to adversarial
samples, indistinguishable from real samples to human eye, adversarial samples
lead to in... | computer science |
3,446 | Cross-label Suppression: A Discriminative and Fast Dictionary Learning
with Group Regularization | cs.LG | This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for differe... | computer science |
3,447 | Learning Deep Networks from Noisy Labels with Dropout Regularization | cs.CV | Large datasets often have unreliable labels-such as those obtained from
Amazon's Mechanical Turk or social media platforms-and classifiers trained on
mislabeled datasets often exhibit poor performance. We present a simple,
effective technique for accounting for label noise when training deep neural
networks. We augment... | computer science |
3,448 | Real-Time Adaptive Image Compression | stat.ML | We present a machine learning-based approach to lossy image compression which
outperforms all existing codecs, while running in real-time.
Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG
2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of
generic images across al... | computer science |
3,449 | Regularizing deep networks using efficient layerwise adversarial
training | cs.CV | Adversarial training has been shown to regularize deep neural networks in
addition to increasing their robustness to adversarial examples. However, its
impact on very deep state of the art networks has not been fully investigated.
In this paper, we present an efficient approach to perform adversarial training
by pertur... | computer science |
3,450 | Hashing as Tie-Aware Learning to Rank | stat.ML | Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first o... | computer science |
3,451 | Unsupervised Learning Layers for Video Analysis | cs.LG | This paper presents two unsupervised learning layers (UL layers) for
label-free video analysis: one for fully connected layers, and the other for
convolutional ones. The proposed UL layers can play two roles: they can be the
cost function layer for providing global training signal; meanwhile they can be
added to any re... | computer science |
3,452 | Gated XNOR Networks: Deep Neural Networks with Ternary Weights and
Activations under a Unified Discretization Framework | cs.LG | There is a pressing need to build an architecture that could subsume these
networks undera unified framework that achieves both higher performance and
less overhead. To this end, two fundamental issues are yet to be addressed. The
first one is how to implement the back propagation when neuronal activations
are discrete... | computer science |
3,453 | Conditional CycleGAN for Attribute Guided Face Image Generation | cs.CV | State-of-the-art techniques in Generative Adversarial Networks (GANs) such as
cycleGAN is able to learn the mapping of one image domain $X$ to another image
domain $Y$ using unpaired image data. We extend the cycleGAN to ${\it
Conditional}$ cycleGAN such that the mapping from $X$ to $Y$ is subjected to
attribute condit... | computer science |
3,454 | Generative Models of Visually Grounded Imagination | cs.LG | It is easy for people to imagine what a man with pink hair looks like, even
if they have never seen such a person before. We call the ability to create
images of novel semantic concepts visually grounded imagination. In this paper,
we show how we can modify variational auto-encoders to perform this task. Our
method use... | computer science |
3,455 | Deep Generative Adversarial Networks for Compressed Sensing Automates
MRI | cs.CV | Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear
inverse task demanding time and resource intensive computations that can
substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In
addition, state-of-the-art compressed sensing (CS) analytics are not cognizant
of the imag... | computer science |
3,456 | Hyperplane Clustering Via Dual Principal Component Pursuit | cs.CV | We extend the theoretical analysis of a recently proposed single subspace
learning algorithm, called Dual Principal Component Pursuit (DPCP), to the case
where the data are drawn from of a union of hyperplanes. To gain insight into
the properties of the $\ell_1$ non-convex problem associated with DPCP, we
develop a geo... | computer science |
3,457 | Low-shot learning with large-scale diffusion | cs.CV | This paper considers the problem of inferring image labels for which only a
few labelled examples are available at training time. This setup is often
referred to as low-shot learning in the literature, where a standard approach
is to re-train the last few layers of a convolutional neural network learned on
separate cla... | computer science |
3,458 | Training Quantized Nets: A Deeper Understanding | cs.LG | Currently, deep neural networks are deployed on low-power portable devices by
first training a full-precision model using powerful hardware, and then
deriving a corresponding low-precision model for efficient inference on such
systems. However, training models directly with coarsely quantized weights is a
key step towa... | computer science |
3,459 | Poseidon: An Efficient Communication Architecture for Distributed Deep
Learning on GPU Clusters | cs.LG | Deep learning models can take weeks to train on a single GPU-equipped
machine, necessitating scaling out DL training to a GPU-cluster. However,
current distributed DL implementations can scale poorly due to substantial
parameter synchronization over the network, because the high throughput of GPUs
allows more data batc... | computer science |
3,460 | SmoothGrad: removing noise by adding noise | cs.LG | Explaining the output of a deep network remains a challenge. In the case of
an image classifier, one type of explanation is to identify pixels that
strongly influence the final decision. A starting point for this strategy is
the gradient of the class score function with respect to the input image. This
gradient can be ... | computer science |
3,461 | Deep Learning Methods for Efficient Large Scale Video Labeling | stat.ML | We present a solution to "Google Cloud and YouTube-8M Video Understanding
Challenge" that ranked 5th place. The proposed model is an ensemble of three
model families, two frame level and one video level. The training was performed
on augmented dataset, with cross validation. | computer science |
3,462 | SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced
Learning | cs.CV | It is known that Boosting can be interpreted as a gradient descent technique
to minimize an underlying loss function. Specifically, the underlying loss
being minimized by the traditional AdaBoost is the exponential loss, which is
proved to be very sensitive to random noise/outliers. Therefore, several
Boosting algorith... | computer science |
3,463 | Chemception: A Deep Neural Network with Minimal Chemistry Knowledge
Matches the Performance of Expert-developed QSAR/QSPR Models | stat.ML | In the last few years, we have seen the transformative impact of deep
learning in many applications, particularly in speech recognition and computer
vision. Inspired by Google's Inception-ResNet deep convolutional neural network
(CNN) for image classification, we have developed "Chemception", a deep CNN for
the predict... | computer science |
3,464 | Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study | stat.ML | Deep neural networks (DNNs) have achieved unprecedented performance on a wide
range of complex tasks, rapidly outpacing our understanding of the nature of
their solutions. This has caused a recent surge of interest in methods for
rendering modern neural systems more interpretable. In this work, we propose to
address th... | computer science |
3,465 | On Measuring and Quantifying Performance: Error Rates, Surrogate Loss,
and an Example in SSL | cs.LG | In various approaches to learning, notably in domain adaptation, active
learning, learning under covariate shift, semi-supervised learning, learning
with concept drift, and the like, one often wants to compare a baseline
classifier to one or more advanced (or at least different) strategies. In this
chapter, we basicall... | computer science |
3,466 | Foolbox: A Python toolbox to benchmark the robustness of machine
learning models | cs.LG | Even todays most advanced machine learning models are easily fooled by almost
imperceptible perturbations of their inputs. Foolbox is a new Python package to
generate such adversarial perturbations and to quantify and compare the
robustness of machine learning models. It is build around the idea that the
most comparabl... | computer science |
3,467 | Optimizing the Latent Space of Generative Networks | stat.ML | Generative Adversarial Networks (GANs) have been shown to be able to sample
impressively realistic images. GAN training consists of a saddle point
optimization problem that can be thought of as an adversarial game between a
generator which produces the images, and a discriminator, which judges if the
images are real. B... | computer science |
3,468 | Resting state fMRI functional connectivity-based classification using a
convolutional neural network architecture | stat.ML | Machine learning techniques have become increasingly popular in the field of
resting state fMRI (functional magnetic resonance imaging) network based
classification. However, the application of convolutional networks has been
proposed only very recently and has remained largely unexplored. In this paper
we describe a c... | computer science |
3,469 | A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics | stat.ML | Existing dimensionality reduction methods are adept at revealing hidden
underlying manifolds arising from high-dimensional data and thereby producing a
low-dimensional representation. However, the smoothness of the manifolds
produced by classic techniques in the presence of noise is not guaranteed. In
fact, the embeddi... | computer science |
3,470 | Learning Transferable Architectures for Scalable Image Recognition | cs.CV | Developing neural network image classification models often requires
significant architecture engineering. In this paper, we attempt to automate
this engineering process by learning the model architectures directly on the
dataset of interest. As this approach is expensive when the dataset is large,
we propose to search... | computer science |
3,471 | A Simple Exponential Family Framework for Zero-Shot Learning | cs.LG | We present a simple generative framework for learning to predict previously
unseen classes, based on estimating class-attribute-gated class-conditional
distributions. We model each class-conditional distribution as an exponential
family distribution and the parameters of the distribution of each seen/unseen
class are d... | computer science |
3,472 | Learning Robust Representations for Computer Vision | stat.ML | Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
Our main goal is deeper understanding and new development of robust
... | computer science |
3,473 | Tensorial Recurrent Neural Networks for Longitudinal Data Analysis | cs.LG | Traditional Recurrent Neural Networks assume vectorized data as inputs.
However many data from modern science and technology come in certain structures
such as tensorial time series data. To apply the recurrent neural networks for
this type of data, a vectorisation process is necessary, while such a
vectorisation leads... | computer science |
3,474 | Active Learning for Convolutional Neural Networks: A Core-Set Approach | stat.ML | Convolutional neural networks (CNNs) have been successfully applied to many
recognition and learning tasks using a universal recipe; training a deep model
on a very large dataset of supervised examples. However, this approach is
rather restrictive in practice since collecting a large set of labeled images
is very expen... | computer science |
3,475 | Controllable Generative Adversarial Network | cs.LG | Although it is recently introduced, in last few years, generative adversarial
network (GAN) has been shown many promising results to generate realistic
samples. However, it is hardly able to control generated samples since input
variables for a generator are from a random distribution. Some attempts have
been made to c... | computer science |
3,476 | A Latent Variable Model for Two-Dimensional Canonical Correlation
Analysis and its Variational Inference | cs.CV | Describing the dimension reduction (DR) techniques by means of probabilistic
models has recently been given special attention. Probabilistic models, in
addition to a better interpretability of the DR methods, provide a framework
for further extensions of such algorithms. One of the new approaches to the
probabilistic D... | computer science |
3,477 | Jointly Attentive Spatial-Temporal Pooling Networks for Video-based
Person Re-Identification | cs.CV | Person Re-Identification (person re-id) is a crucial task as its applications
in visual surveillance and human-computer interaction. In this work, we present
a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for
video-based person re-identification, which enables the feature extractor to be
aware of ... | computer science |
3,478 | Improved Fixed-Rank Nyström Approximation via QR Decomposition:
Practical and Theoretical Aspects | stat.ML | The Nystr\"om method is a popular technique for computing fixed-rank
approximations of large kernel matrices using a small number of landmark
points. In practice, to ensure high quality approximations, the number of
landmark points is chosen to be greater than the target rank. However, the
standard Nystr\"om method use... | computer science |
3,479 | Deep Incremental Boosting | stat.ML | This paper introduces Deep Incremental Boosting, a new technique derived from
AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
the required training time and improves generalisation. We draw inspiration
from Transfer of Learning approaches to reduce the start-up time to training
each incr... | computer science |
3,480 | Augmentor: An Image Augmentation Library for Machine Learning | cs.CV | The generation of artificial data based on existing observations, known as
data augmentation, is a technique used in machine learning to improve model
accuracy, generalisation, and to control overfitting. Augmentor is a software
package, available in both Python and Julia versions, that provides a high
level API for th... | computer science |
3,481 | GANs for Biological Image Synthesis | cs.CV | In this paper, we propose a novel application of Generative Adversarial
Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy.
Compared to natural images, cells tend to have a simpler and more geometric
global structure that facilitates image generation. However, the correlation
between the spatial... | computer science |
3,482 | Large Margin Learning in Set to Set Similarity Comparison for Person
Re-identification | cs.CV | Person re-identification (Re-ID) aims at matching images of the same person
across disjoint camera views, which is a challenging problem in multimedia
analysis, multimedia editing and content-based media retrieval communities. The
major challenge lies in how to preserve similarity of the same person across
video footag... | computer science |
3,483 | Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms | cs.LG | We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
of 70,000 fashion products from 10 categories, with 7,000 images per category.
The training set has 60,000 images and the test set has 10,000 images.
Fashion-MNIST is intended to serve as a direct drop-in replacement for the
original MNIST dat... | computer science |
3,484 | Imbalanced Malware Images Classification: a CNN based Approach | cs.CV | Deep convolutional neural networks (CNNs) can be applied to malware binary
detection through images classification. The performance, however, is degraded
due to the imbalance of malware families (classes). To mitigate this issue, we
propose a simple yet effective weighted softmax loss which can be employed as
the final... | computer science |
3,485 | Open-World Visual Recognition Using Knowledge Graphs | cs.LG | In a real-world setting, visual recognition systems can be brought to make
predictions for images belonging to previously unknown class labels. In order
to make semantically meaningful predictions for such inputs, we propose a
two-step approach that utilizes information from knowledge graphs. First, a
knowledge-graph r... | computer science |
3,486 | Deep Learning Sparse Ternary Projections for Compressed Sensing of
Images | cs.CV | Compressed sensing (CS) is a sampling theory that allows reconstruction of
sparse (or compressible) signals from an incomplete number of measurements,
using of a sensing mechanism implemented by an appropriate projection matrix.
The CS theory is based on random Gaussian projection matrices, which satisfy
recovery guara... | computer science |
3,487 | Framing U-Net via Deep Convolutional Framelets: Application to
Sparse-view CT | cs.CV | X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using la... | computer science |
3,488 | On denoising autoencoders trained to minimise binary cross-entropy | cs.CV | Denoising autoencoders (DAEs) are powerful deep learning models used for
feature extraction, data generation and network pre-training. DAEs consist of
an encoder and decoder which may be trained simultaneously to minimise a loss
(function) between an input and the reconstruction of a corrupted version of
the input. The... | computer science |
3,489 | Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary
Learning | cs.CV | The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) model,
consisting of a cascade of convolutional sparse layers, provides a new
interpretation of Convolutional Neural Networks (CNNs). Under this framework,
the computation of the forward pass in a CNN is equivalent to a pursuit
algorithm aiming to e... | computer science |
3,490 | Multi-view Low-rank Sparse Subspace Clustering | cs.CV | Most existing approaches address multi-view subspace clustering problem by
constructing the affinity matrix on each view separately and afterwards propose
how to extend spectral clustering algorithm to handle multi-view data. This
paper presents an approach to multi-view subspace clustering that learns a
joint subspace... | computer science |
3,491 | Hierarchical loss for classification | cs.LG | Failing to distinguish between a sheepdog and a skyscraper should be worse
and penalized more than failing to distinguish between a sheepdog and a poodle;
after all, sheepdogs and poodles are both breeds of dogs. However, existing
metrics of failure (so-called "loss" or "win") used in textual or visual
classification/r... | computer science |
3,492 | Learning to Compose Domain-Specific Transformations for Data
Augmentation | stat.ML | Data augmentation is a ubiquitous technique for increasing the size of
labeled training sets by leveraging task-specific data transformations that
preserve class labels. While it is often easy for domain experts to specify
individual transformations, constructing and tuning the more sophisticated
compositions typically... | computer science |
3,493 | Deep learning from crowds | stat.ML | Over the last few years, deep learning has revolutionized the field of
machine learning by dramatically improving the state-of-the-art in various
domains. However, as the size of supervised artificial neural networks grows,
typically so does the need for larger labeled datasets. Recently, crowdsourcing
has established ... | computer science |
3,494 | Clustering of Data with Missing Entries using Non-convex Fusion
Penalties | cs.CV | The presence of missing entries in data often creates challenges for pattern
recognition algorithms. Traditional algorithms for clustering data assume that
all the feature values are known for every data point. We propose a method to
cluster data in the presence of missing information. Unlike conventional
clustering te... | computer science |
3,495 | Adaptive PCA for Time-Varying Data | stat.ML | In this paper, we present an online adaptive PCA algorithm that is able to
compute the full dimensional eigenspace per new time-step of sequential data.
The algorithm is based on a one-step update rule that considers all second
order correlations between previous samples and the new time-step. Our
algorithm has O(n) co... | computer science |
3,496 | Simultaneously Learning Neighborship and Projection Matrix for
Supervised Dimensionality Reduction | cs.CV | Explicitly or implicitly, most of dimensionality reduction methods need to
determine which samples are neighbors and the similarity between the neighbors
in the original highdimensional space. The projection matrix is then learned on
the assumption that the neighborhood information (e.g., the similarity) is
known and f... | computer science |
3,497 | Co-training for Demographic Classification Using Deep Learning from
Label Proportions | cs.CV | Deep learning algorithms have recently produced state-of-the-art accuracy in
many classification tasks, but this success is typically dependent on access to
many annotated training examples. For domains without such data, an attractive
alternative is to train models with light, or distant supervision. In this
paper, we... | computer science |
3,498 | Subspace Clustering using Ensembles of $K$-Subspaces | cs.CV | We present a novel approach to the subspace clustering problem that leverages
ensembles of the $K$-subspaces (KSS) algorithm via the evidence accumulation
clustering framework. Our algorithm forms a co-association matrix whose
$(i,j)$th entry is the number of times points $i$ and $j$ are clustered
together by several r... | computer science |
3,499 | Informed Non-convex Robust Principal Component Analysis with Features | stat.ML | We revisit the problem of robust principal component analysis with features
acting as prior side information. To this aim, a novel, elegant, non-convex
optimization approach is proposed to decompose a given observation matrix into
a low-rank core and the corresponding sparse residual. Rigorous theoretical
analysis of t... | computer science |
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