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3,300 | Clustering based on the In-tree Graph Structure and Affinity Propagation | cs.LG | A recently proposed clustering method, called the Nearest Descent (ND), can
organize the whole dataset into a sparsely connected graph, called the In-tree.
This ND-based Intree structure proves able to reveal the clustering structure
underlying the dataset, except one imperfect place, that is, there are some
undesired ... | computer science |
3,301 | Microscopic Advances with Large-Scale Learning: Stochastic Optimization
for Cryo-EM | stat.ML | Determining the 3D structures of biological molecules is a key problem for
both biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising
technique for structure estimation which relies heavily on computational
methods to reconstruct 3D structures from 2D images. This paper introduces the
challenging Cryo-... | computer science |
3,302 | Optimizing affinity-based binary hashing using auxiliary coordinates | cs.LG | In supervised binary hashing, one wants to learn a function that maps a
high-dimensional feature vector to a vector of binary codes, for application to
fast image retrieval. This typically results in a difficult optimization
problem, nonconvex and nonsmooth, because of the discrete variables involved.
Much work has sim... | computer science |
3,303 | Bi-Objective Nonnegative Matrix Factorization: Linear Versus
Kernel-Based Models | stat.ML | Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this... | computer science |
3,304 | IT-map: an Effective Nonlinear Dimensionality Reduction Method for
Interactive Clustering | stat.ML | Scientists in many fields have the common and basic need of dimensionality
reduction: visualizing the underlying structure of the massive multivariate
data in a low-dimensional space. However, many dimensionality reduction methods
confront the so-called "crowding problem" that clusters tend to overlap with
each other i... | computer science |
3,305 | Efficient SDP Inference for Fully-connected CRFs Based on Low-rank
Decomposition | cs.CV | Conditional Random Fields (CRF) have been widely used in a variety of
computer vision tasks. Conventional CRFs typically define edges on neighboring
image pixels, resulting in a sparse graph such that efficient inference can be
performed. However, these CRFs fail to model long-range contextual
relationships. Fully-conn... | computer science |
3,306 | Caffe con Troll: Shallow Ideas to Speed Up Deep Learning | cs.LG | We present Caffe con Troll (CcT), a fully compatible end-to-end version of
the popular framework Caffe with rebuilt internals. We built CcT to examine the
performance characteristics of training and deploying general-purpose
convolutional neural networks across different hardware architectures. We find
that, by employi... | computer science |
3,307 | Becoming the Expert - Interactive Multi-Class Machine Teaching | cs.CV | Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important... | computer science |
3,308 | Semi-Orthogonal Multilinear PCA with Relaxed Start | stat.ML | Principal component analysis (PCA) is an unsupervised method for learning
low-dimensional features with orthogonal projections. Multilinear PCA methods
extend PCA to deal with multidimensional data (tensors) directly via
tensor-to-tensor projection or tensor-to-vector projection (TVP). However,
under the TVP setting, i... | computer science |
3,309 | Hierarchical Subquery Evaluation for Active Learning on a Graph | cs.CV | To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between ru... | computer science |
3,310 | Deeply Learning the Messages in Message Passing Inference | cs.CV | Deep structured output learning shows great promise in tasks like semantic
image segmentation. We proffer a new, efficient deep structured model learning
scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be
used to estimate the messages in message passing inference for structured
prediction wit... | computer science |
3,311 | Learning with Group Invariant Features: A Kernel Perspective | cs.LG | We analyze in this paper a random feature map based on a theory of invariance
I-theory introduced recently. More specifically, a group invariant signal
signature is obtained through cumulative distributions of group transformed
random projections. Our analysis bridges invariant feature learning with kernel
methods, as ... | computer science |
3,312 | Learning to Select Pre-Trained Deep Representations with Bayesian
Evidence Framework | cs.CV | We propose a Bayesian evidence framework to facilitate transfer learning from
pre-trained deep convolutional neural networks (CNNs). Our framework is
formulated on top of a least squares SVM (LS-SVM) classifier, which is simple
and fast in both training and testing, and achieves competitive performance in
practice. The... | computer science |
3,313 | Generative Image Modeling Using Spatial LSTMs | stat.ML | Modeling the distribution of natural images is challenging, partly because of
strong statistical dependencies which can extend over hundreds of pixels.
Recurrent neural networks have been successful in capturing long-range
dependencies in a number of problems but only recently have found their way
into generative image... | computer science |
3,314 | Bayesian representation learning with oracle constraints | stat.ML | Representation learning systems typically rely on massive amounts of labeled
data in order to be trained to high accuracy. Recently, high-dimensional
parametric models like neural networks have succeeded in building rich
representations using either compressive, reconstructive or supervised
criteria. However, the seman... | computer science |
3,315 | Learning with a Wasserstein Loss | cs.LG | Learning to predict multi-label outputs is challenging, but in many problems
there is a natural metric on the outputs that can be used to improve
predictions. In this paper we develop a loss function for multi-label learning,
based on the Wasserstein distance. The Wasserstein distance provides a natural
notion of dissi... | computer science |
3,316 | A general framework for the IT-based clustering methods | cs.CV | Previously, we proposed a physically inspired rule to organize the data
points in a sparse yet effective structure, called the in-tree (IT) graph,
which is able to capture a wide class of underlying cluster structures in the
datasets, especially for the density-based datasets. Although there are some
redundant edges or... | computer science |
3,317 | Aligning where to see and what to tell: image caption with region-based
attention and scene factorization | cs.CV | Recent progress on automatic generation of image captions has shown that it
is possible to describe the most salient information conveyed by images with
accurate and meaningful sentences. In this paper, we propose an image caption
system that exploits the parallel structures between images and sentences. In
our model, ... | computer science |
3,318 | Embed to Control: A Locally Linear Latent Dynamics Model for Control
from Raw Images | cs.LG | We introduce Embed to Control (E2C), a method for model learning and control
of non-linear dynamical systems from raw pixel images. E2C consists of a deep
generative model, belonging to the family of variational autoencoders, that
learns to generate image trajectories from a latent space in which the dynamics
is constr... | computer science |
3,319 | A Novel Approach for Stable Selection of Informative Redundant Features
from High Dimensional fMRI Data | cs.CV | Feature selection is among the most important components because it not only
helps enhance the classification accuracy, but also or even more important
provides potential biomarker discovery. However, traditional multivariate
methods is likely to obtain unstable and unreliable results in case of an
extremely high dimen... | computer science |
3,320 | LogDet Rank Minimization with Application to Subspace Clustering | cs.CV | Low-rank matrix is desired in many machine learning and computer vision
problems. Most of the recent studies use the nuclear norm as a convex surrogate
of the rank operator. However, all singular values are simply added together by
the nuclear norm, and thus the rank may not be well approximated in practical
problems. ... | computer science |
3,321 | Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit | cs.CV | Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm
regularization have become very popular due to their simplicity, theoretical
guarantees and empirical success. However, the choice of the regularizer can
greatly impact both theory and practice. For instance, $\ell_1$ regularization
is guaranteed t... | computer science |
3,322 | Banzhaf Random Forests | cs.LG | Random forests are a type of ensemble method which makes predictions by
combining the results of several independent trees. However, the theory of
random forests has long been outpaced by their application. In this paper, we
propose a novel random forests algorithm based on cooperative game theory.
Banzhaf power index ... | computer science |
3,323 | Manitest: Are classifiers really invariant? | cs.CV | Invariance to geometric transformations is a highly desirable property of
automatic classifiers in many image recognition tasks. Nevertheless, it is
unclear to which extent state-of-the-art classifiers are invariant to basic
transformations such as rotations and translations. This is mainly due to the
lack of general m... | computer science |
3,324 | IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family | stat.ML | Previously, we proposed a physically-inspired method to construct data points
into an effective in-tree (IT) structure, in which the underlying cluster
structure in the dataset is well revealed. Although there are some edges in the
IT structure requiring to be removed, such undesired edges are generally
distinguishable... | computer science |
3,325 | A Generative Model for Multi-Dialect Representation | cs.CV | In the era of deep learning several unsupervised models have been developed
to capture the key features in unlabeled handwritten data. Popular among them
is the Restricted Boltzmann Machines RBM. However, due to the novelty in
handwritten multidialect data, the RBM may fail to generate an efficient
representation. In t... | computer science |
3,326 | Multi-criteria Similarity-based Anomaly Detection using Pareto Depth
Analysis | cs.CV | We consider the problem of identifying patterns in a data set that exhibit
anomalous behavior, often referred to as anomaly detection. Similarity-based
anomaly detection algorithms detect abnormally large amounts of similarity or
dissimilarity, e.g.~as measured by nearest neighbor Euclidean distances between
a test sam... | computer science |
3,327 | DeepWriterID: An End-to-end Online Text-independent Writer
Identification System | cs.CV | Owing to the rapid growth of touchscreen mobile terminals and pen-based
interfaces, handwriting-based writer identification systems are attracting
increasing attention for personal authentication, digital forensics, and other
applications. However, most studies on writer identification have not been
satisfying because ... | computer science |
3,328 | Learning A Task-Specific Deep Architecture For Clustering | cs.LG | While sparse coding-based clustering methods have shown to be successful,
their bottlenecks in both efficiency and scalability limit the practical usage.
In recent years, deep learning has been proved to be a highly effective,
efficient and scalable feature learning tool. In this paper, we propose to
emulate the sparse... | computer science |
3,329 | EM Algorithms for Weighted-Data Clustering with Application to
Audio-Visual Scene Analysis | cs.CV | Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-m... | computer science |
3,330 | Clustering by Hierarchical Nearest Neighbor Descent (H-NND) | stat.ML | Previously in 2014, we proposed the Nearest Descent (ND) method, capable of
generating an efficient Graph, called the in-tree (IT). Due to some beautiful
and effective features, this IT structure proves well suited for data
clustering. Although there exist some redundant edges in IT, they usually have
salient features ... | computer science |
3,331 | A deep matrix factorization method for learning attribute
representations | cs.CV | Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lo... | computer science |
3,332 | Large-scale subspace clustering using sketching and validation | cs.LG | The nowadays massive amounts of generated and communicated data present major
challenges in their processing. While capable of successfully classifying
nonlinearly separable objects in various settings, subspace clustering (SC)
methods incur prohibitively high computational complexity when processing
large-scale data. ... | computer science |
3,333 | Structured Transforms for Small-Footprint Deep Learning | stat.ML | We consider the task of building compact deep learning pipelines suitable for
deployment on storage and power constrained mobile devices. We propose a
unified framework to learn a broad family of structured parameter matrices that
are characterized by the notion of low displacement rank. Our structured
transforms admit... | computer science |
3,334 | Texture Modelling with Nested High-order Markov-Gibbs Random Fields | cs.CV | Currently, Markov-Gibbs random field (MGRF) image models which include
high-order interactions are almost always built by modelling responses of a
stack of local linear filters. Actual interaction structure is specified
implicitly by the filter coefficients. In contrast, we learn an explicit
high-order MGRF structure b... | computer science |
3,335 | The Wilson Machine for Image Modeling | stat.ML | Learning the distribution of natural images is one of the hardest and most
important problems in machine learning. The problem remains open, because the
enormous complexity of the structures in natural images spans all length
scales. We break down the complexity of the problem and show that the hierarchy
of structures ... | computer science |
3,336 | Properties of the Sample Mean in Graph Spaces and the
Majorize-Minimize-Mean Algorithm | cs.CV | One of the most fundamental concepts in statistics is the concept of sample
mean. Properties of the sample mean that are well-defined in Euclidean spaces
become unwieldy or even unclear in graph spaces. Open problems related to the
sample mean of graphs include: non-existence, non-uniqueness, statistical
inconsistency,... | computer science |
3,337 | Multiple Instance Dictionary Learning using Functions of Multiple
Instances | cs.CV | A multiple instance dictionary learning method using functions of multiple
instances (DL-FUMI) is proposed to address target detection and two-class
classification problems with inaccurate training labels. Given inaccurate
training labels, DL-FUMI learns a set of target dictionary atoms that describe
the most distincti... | computer science |
3,338 | Deep Mean Maps | stat.ML | The use of distributions and high-level features from deep architecture has
become commonplace in modern computer vision. Both of these methodologies have
separately achieved a great deal of success in many computer vision tasks.
However, there has been little work attempting to leverage the power of these
to methodolo... | computer science |
3,339 | Robust PCA via Nonconvex Rank Approximation | cs.CV | Numerous applications in data mining and machine learning require recovering
a matrix of minimal rank. Robust principal component analysis (RPCA) is a
general framework for handling this kind of problems. Nuclear norm based convex
surrogate of the rank function in RPCA is widely investigated. Under certain
assumptions,... | computer science |
3,340 | Deep multi-scale video prediction beyond mean square error | cs.LG | Learning to predict future images from a video sequence involves the
construction of an internal representation that models the image evolution
accurately, and therefore, to some degree, its content and dynamics. This is
why pixel-space video prediction may be viewed as a promising avenue for
unsupervised feature learn... | computer science |
3,341 | Convolutional neural networks with low-rank regularization | cs.LG | Large CNNs have delivered impressive performance in various computer vision
applications. But the storage and computation requirements make it problematic
for deploying these models on mobile devices. Recently, tensor decompositions
have been used for speeding up CNNs. In this paper, we further develop the
tensor decom... | computer science |
3,342 | Multimodal sparse representation learning and applications | cs.LG | Unsupervised methods have proven effective for discriminative tasks in a
single-modality scenario. In this paper, we present a multimodal framework for
learning sparse representations that can capture semantic correlation between
modalities. The framework can model relationships at a higher level by forcing
the shared ... | computer science |
3,343 | Deep Manifold Traversal: Changing Labels with Convolutional Features | cs.LG | Many tasks in computer vision can be cast as a "label changing" problem,
where the goal is to make a semantic change to the appearance of an image or
some subject in an image in order to alter the class membership. Although
successful task-specific methods have been developed for some label changing
applications, to da... | computer science |
3,344 | Fast Metric Learning For Deep Neural Networks | cs.LG | Similarity metrics are a core component of many information retrieval and
machine learning systems. In this work we propose a method capable of learning
a similarity metric from data equipped with a binary relation. By considering
only the similarity constraints, and initially ignoring the features, we are
able to lear... | computer science |
3,345 | Top-k Multiclass SVM | stat.ML | Class ambiguity is typical in image classification problems with a large
number of classes. When classes are difficult to discriminate, it makes sense
to allow k guesses and evaluate classifiers based on the top-k error instead of
the standard zero-one loss. We propose top-k multiclass SVM as a direct method
to optimiz... | computer science |
3,346 | Loss Functions for Top-k Error: Analysis and Insights | stat.ML | In order to push the performance on realistic computer vision tasks, the
number of classes in modern benchmark datasets has significantly increased in
recent years. This increase in the number of classes comes along with increased
ambiguity between the class labels, raising the question if top-1 error is the
right perf... | computer science |
3,347 | A Large Dataset to Train Convolutional Networks for Disparity, Optical
Flow, and Scene Flow Estimation | cs.CV | Recent work has shown that optical flow estimation can be formulated as a
supervised learning task and can be successfully solved with convolutional
networks. Training of the so-called FlowNet was enabled by a large
synthetically generated dataset. The present paper extends the concept of
optical flow estimation via co... | computer science |
3,348 | Pseudo-Bayesian Robust PCA: Algorithms and Analyses | cs.CV | Commonly used in computer vision and other applications, robust PCA
represents an algorithmic attempt to reduce the sensitivity of classical PCA to
outliers. The basic idea is to learn a decomposition of some data matrix of
interest into low rank and sparse components, the latter representing unwanted
outliers. Althoug... | computer science |
3,349 | Implementation of deep learning algorithm for automatic detection of
brain tumors using intraoperative IR-thermal mapping data | cs.CV | The efficiency of deep machine learning for automatic delineation of tumor
areas has been demonstrated for intraoperative neuronavigation using active
IR-mapping with the use of the cold test. The proposed approach employs a
matrix IR-imager to remotely register the space-time distribution of surface
temperature patter... | computer science |
3,350 | A Deep Generative Deconvolutional Image Model | cs.CV | A deep generative model is developed for representation and analysis of
images, based on a hierarchical convolutional dictionary-learning framework.
Stochastic {\em unpooling} is employed to link consecutive layers in the model,
yielding top-down image generation. A Bayesian support vector machine is linked
to the top-... | computer science |
3,351 | A Latent-Variable Lattice Model | cs.LG | Markov random field (MRF) learning is intractable, and its approximation
algorithms are computationally expensive. We target a small subset of MRF that
is used frequently in computer vision. We characterize this subset with three
concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as
an alternati... | computer science |
3,352 | Autoencoding beyond pixels using a learned similarity metric | cs.LG | We present an autoencoder that leverages learned representations to better
measure similarities in data space. By combining a variational autoencoder with
a generative adversarial network we can use learned feature representations in
the GAN discriminator as basis for the VAE reconstruction objective. Thereby,
we repla... | computer science |
3,353 | Improved graph-based SFA: Information preservation complements the
slowness principle | cs.CV | Slow feature analysis (SFA) is an unsupervised-learning algorithm that
extracts slowly varying features from a multi-dimensional time series. A
supervised extension to SFA for classification and regression is graph-based
SFA (GSFA). GSFA is based on the preservation of similarities, which are
specified by a graph struc... | computer science |
3,354 | Understanding Deep Convolutional Networks | stat.ML | Deep convolutional networks provide state of the art classifications and
regressions results over many high-dimensional problems. We review their
architecture, which scatters data with a cascade of linear filter weights and
non-linearities. A mathematical framework is introduced to analyze their
properties. Computation... | computer science |
3,355 | An ensemble diversity approach to supervised binary hashing | cs.LG | Binary hashing is a well-known approach for fast approximate nearest-neighbor
search in information retrieval. Much work has focused on affinity-based
objective functions involving the hash functions or binary codes. These
objective functions encode neighborhood information between data points and are
often inspired by... | computer science |
3,356 | Random Feature Maps via a Layered Random Projection (LaRP) Framework for
Object Classification | cs.CV | The approximation of nonlinear kernels via linear feature maps has recently
gained interest due to their applications in reducing the training and testing
time of kernel-based learning algorithms. Current random projection methods
avoid the curse of dimensionality by embedding the nonlinear feature space into
a low dim... | computer science |
3,357 | Ensemble Robustness and Generalization of Stochastic Deep Learning
Algorithms | cs.LG | The question why deep learning algorithms generalize so well has attracted
increasing research interest. However, most of the well-established approaches,
such as hypothesis capacity, stability or sparseness, have not provided
complete explanations (Zhang et al., 2016; Kawaguchi et al., 2017). In this
work, we focus on... | computer science |
3,358 | Learning deep representation of multityped objects and tasks | stat.ML | We introduce a deep multitask architecture to integrate multityped
representations of multimodal objects. This multitype exposition is less
abstract than the multimodal characterization, but more machine-friendly, and
thus is more precise to model. For example, an image can be described by
multiple visual views, which ... | computer science |
3,359 | Variational methods for Conditional Multimodal Deep Learning | cs.CV | In this paper, we address the problem of conditional modality learning,
whereby one is interested in generating one modality given the other. While it
is straightforward to learn a joint distribution over multiple modalities using
a deep multimodal architecture, we observe that such models aren't very
effective at cond... | computer science |
3,360 | Ensemble of Deep Convolutional Neural Networks for Learning to Detect
Retinal Vessels in Fundus Images | cs.LG | Vision impairment due to pathological damage of the retina can largely be
prevented through periodic screening using fundus color imaging. However the
challenge with large scale screening is the inability to exhaustively detect
fine blood vessels crucial to disease diagnosis. In this work we present a
computational ima... | computer science |
3,361 | An end-to-end convolutional selective autoencoder approach to Soybean
Cyst Nematode eggs detection | cs.CV | This paper proposes a novel selective autoencoder approach within the
framework of deep convolutional networks. The crux of the idea is to train a
deep convolutional autoencoder to suppress undesired parts of an image frame
while allowing the desired parts resulting in efficient object detection. The
efficacy of the fr... | computer science |
3,362 | Deep Convolutional Neural Networks on Cartoon Functions | cs.LG | Wiatowski and B\"olcskei, 2015, proved that deformation stability and
vertical translation invariance of deep convolutional neural network-based
feature extractors are guaranteed by the network structure per se rather than
the specific convolution kernels and non-linearities. While the translation
invariance result app... | computer science |
3,363 | Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace
Clustering | cs.LG | State-of-the-art subspace clustering methods are based on expressing each
data point as a linear combination of other data points while regularizing the
matrix of coefficients with $\ell_1$, $\ell_2$ or nuclear norms. $\ell_1$
regularization is guaranteed to give a subspace-preserving affinity (i.e.,
there are no conne... | computer science |
3,364 | A Theoretical Analysis of Deep Neural Networks for Texture
Classification | cs.CV | We investigate the use of Deep Neural Networks for the classification of
image datasets where texture features are important for generating
class-conditional discriminative representations. To this end, we first derive
the size of the feature space for some standard textural features extracted
from the input dataset an... | computer science |
3,365 | Incremental Robot Learning of New Objects with Fixed Update Time | stat.ML | We consider object recognition in the context of lifelong learning, where a
robotic agent learns to discriminate between a growing number of object classes
as it accumulates experience about the environment. We propose an incremental
variant of the Regularized Least Squares for Classification (RLSC) algorithm,
and expl... | computer science |
3,366 | End-to-End Kernel Learning with Supervised Convolutional Kernel Networks | stat.ML | In this paper, we introduce a new image representation based on a multilayer
kernel machine. Unlike traditional kernel methods where data representation is
decoupled from the prediction task, we learn how to shape the kernel with
supervision. We proceed by first proposing improvements of the
recently-introduced convolu... | computer science |
3,367 | A Rapid Pattern-Recognition Method for Driving Types Using
Clustering-Based Support Vector Machines | stat.ML | A rapid pattern-recognition approach to characterize driver's
curve-negotiating behavior is proposed. To shorten the recognition time and
improve the recognition of driving styles, a k-means clustering-based support
vector machine ( kMC-SVM) method is developed and used for classifying drivers
into two types: aggressiv... | computer science |
3,368 | Stochastic Function Norm Regularization of Deep Networks | cs.LG | Deep neural networks have had an enormous impact on image analysis.
State-of-the-art training methods, based on weight decay and DropOut, result in
impressive performance when a very large training set is available. However,
they tend to have large problems overfitting to small data sets. Indeed, the
available regulari... | computer science |
3,369 | Hyperspectral Image Classification with Support Vector Machines on
Kernel Distribution Embeddings | cs.CV | We propose a novel approach for pixel classification in hyperspectral images,
leveraging on both the spatial and spectral information in the data. The
introduced method relies on a recently proposed framework for learning on
distributions -- by representing them with mean elements in reproducing kernel
Hilbert spaces (... | computer science |
3,370 | Integrated perception with recurrent multi-task neural networks | stat.ML | Modern discriminative predictors have been shown to match natural
intelligences in specific perceptual tasks in image classification, object and
part detection, boundary extraction, etc. However, a major advantage that
natural intelligences still have is that they work well for "all" perceptual
problems together, solvi... | computer science |
3,371 | Mutual Exclusivity Loss for Semi-Supervised Deep Learning | cs.CV | In this paper we consider the problem of semi-supervised learning with deep
Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated
on the observation that unlabeled data is cheap and can be used to improve the
accuracy of classifiers. In this paper we propose an unsupervised
regularization term... | computer science |
3,372 | TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse
View X-Ray Applications | math.OC | We present TRex, a flexible and robust Tomographic Reconstruction framework
using proximal algorithms. We provide an overview and perform an experimental
comparison between the famous iterative reconstruction methods in terms of
reconstruction quality in sparse view situations. We then derive the proximal
operators for... | computer science |
3,373 | Combining multiscale features for classification of hyperspectral
images: a sequence based kernel approach | cs.CV | Nowadays, hyperspectral image classification widely copes with spatial
information to improve accuracy. One of the most popular way to integrate such
information is to extract hierarchical features from a multiscale segmentation.
In the classification context, the extracted features are commonly concatenated
into a lon... | computer science |
3,374 | DropNeuron: Simplifying the Structure of Deep Neural Networks | cs.CV | Deep learning using multi-layer neural networks (NNs) architecture manifests
superb power in modern machine learning systems. The trained Deep Neural
Networks (DNNs) are typically large. The question we would like to address is
whether it is possible to simplify the NN during training process to achieve a
reasonable pe... | computer science |
3,375 | Multipartite Ranking-Selection of Low-Dimensional Instances by
Supervised Projection to High-Dimensional Space | stat.ML | Pruning of redundant or irrelevant instances of data is a key to every
successful solution for pattern recognition. In this paper, we present a novel
ranking-selection framework for low-length but highly correlated instances.
Instead of working in the low-dimensional instance space, we learn a supervised
projection to ... | computer science |
3,376 | Learning without Forgetting | cs.CV | When building a unified vision system or gradually adding new capabilities to
a system, the usual assumption is that training data for all tasks is always
available. However, as the number of tasks grows, storing and retraining on
such data becomes infeasible. A new problem arises where we add new
capabilities to a Con... | computer science |
3,377 | Unsupervised Learning of 3D Structure from Images | cs.CV | A key goal of computer vision is to recover the underlying 3D structure from
2D observations of the world. In this paper we learn strong deep generative
models of 3D structures, and recover these structures from 3D and 2D images via
probabilistic inference. We demonstrate high-quality samples and report
log-likelihoods... | computer science |
3,378 | Adversarial examples in the physical world | cs.CV | Most existing machine learning classifiers are highly vulnerable to
adversarial examples. An adversarial example is a sample of input data which
has been modified very slightly in a way that is intended to cause a machine
learning classifier to misclassify it. In many cases, these modifications can
be so subtle that a ... | computer science |
3,379 | Information-theoretical label embeddings for large-scale image
classification | cs.CV | We present a method for training multi-label, massively multi-class image
classification models, that is faster and more accurate than supervision via a
sigmoid cross-entropy loss (logistic regression). Our method consists in
embedding high-dimensional sparse labels onto a lower-dimensional dense sphere
of unit-normed ... | computer science |
3,380 | On the Modeling of Error Functions as High Dimensional Landscapes for
Weight Initialization in Learning Networks | cs.LG | Next generation deep neural networks for classification hosted on embedded
platforms will rely on fast, efficient, and accurate learning algorithms.
Initialization of weights in learning networks has a great impact on the
classification accuracy. In this paper we focus on deriving good initial
weights by modeling the e... | computer science |
3,381 | A Statistical Test for Joint Distributions Equivalence | cs.LG | We provide a distribution-free test that can be used to determine whether any
two joint distributions $p$ and $q$ are statistically different by inspection
of a large enough set of samples. Following recent efforts from Long et al.
[1], we rely on joint kernel distribution embedding to extend the kernel
two-sample test... | computer science |
3,382 | Recurrent Fully Convolutional Neural Networks for Multi-slice MRI
Cardiac Segmentation | stat.ML | In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the ful... | computer science |
3,383 | Generative and Discriminative Voxel Modeling with Convolutional Neural
Networks | cs.CV | When working with three-dimensional data, choice of representation is key. We
explore voxel-based models, and present evidence for the viability of
voxellated representations in applications including shape modeling and object
classification. Our key contributions are methods for training voxel-based
variational autoen... | computer science |
3,384 | Online Feature Selection with Group Structure Analysis | cs.CV | Online selection of dynamic features has attracted intensive interest in
recent years. However, existing online feature selection methods evaluate
features individually and ignore the underlying structure of feature stream.
For instance, in image analysis, features are generated in groups which
represent color, texture... | computer science |
3,385 | Kullback-Leibler Penalized Sparse Discriminant Analysis for
Event-Related Potential Classification | cs.CV | A brain computer interface (BCI) is a system which provides direct
communication between the mind of a person and the outside world by using only
brain activity (EEG). The event-related potential (ERP)-based BCI problem
consists of a binary pattern recognition. Linear discriminant analysis (LDA) is
widely used to solve... | computer science |
3,386 | Robustness of classifiers: from adversarial to random noise | cs.LG | Several recent works have shown that state-of-the-art classifiers are
vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints.
On the other hand, it has been empirically observed that these same classifiers
are relatively robust to random noise. In this paper, we propose to study a
\textit{semi-ran... | computer science |
3,387 | SEBOOST - Boosting Stochastic Learning Using Subspace Optimization
Techniques | cs.CV | We present SEBOOST, a technique for boosting the performance of existing
stochastic optimization methods. SEBOOST applies a secondary optimization
process in the subspace spanned by the last steps and descent directions. The
method was inspired by the SESOP optimization method for large-scale problems,
and has been ada... | computer science |
3,388 | A Probabilistic Optimum-Path Forest Classifier for Binary Classification
Problems | cs.CV | Probabilistic-driven classification techniques extend the role of traditional
approaches that output labels (usually integer numbers) only. Such techniques
are more fruitful when dealing with problems where one is not interested in
recognition/identification only, but also into monitoring the behavior of
consumers and/... | computer science |
3,389 | Unsupervised Monocular Depth Estimation with Left-Right Consistency | cs.CV | Learning based methods have shown very promising results for the task of
depth estimation in single images. However, most existing approaches treat
depth prediction as a supervised regression problem and as a result, require
vast quantities of corresponding ground truth depth data for training. Just
recording quality d... | computer science |
3,390 | Fast and Effective Algorithms for Symmetric Nonnegative Matrix
Factorization | cs.CV | Symmetric Nonnegative Matrix Factorization (SNMF) models arise naturally as
simple reformulations of many standard clustering algorithms including the
popular spectral clustering method. Recent work has demonstrated that an
elementary instance of SNMF provides superior clustering quality compared to
many classic cluste... | computer science |
3,391 | Automatic Construction of a Recurrent Neural Network based Classifier
for Vehicle Passage Detection | cs.CV | Recurrent Neural Networks (RNNs) are extensively used for time-series
modeling and prediction. We propose an approach for automatic construction of a
binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for
detection of a vehicle passage through a checkpoint. As an input to the
classifier we use multidime... | computer science |
3,392 | Amortised MAP Inference for Image Super-resolution | cs.CV | Image super-resolution (SR) is an underdetermined inverse problem, where a
large number of plausible high-resolution images can explain the same
downsampled image. Most current single image SR methods use empirical risk
minimisation, often with a pixel-wise mean squared error (MSE) loss. However,
the outputs from such ... | computer science |
3,393 | Change-point Detection Methods for Body-Worn Video | cs.CV | Body-worn video (BWV) cameras are increasingly utilized by police departments
to provide a record of police-public interactions. However, large-scale BWV
deployment produces terabytes of data per week, necessitating the development
of effective computational methods to identify salient changes in video. In
work carried... | computer science |
3,394 | Temporal Matrix Completion with Locally Linear Latent Factors for
Medical Applications | cs.LG | Regular medical records are useful for medical practitioners to analyze and
monitor patient health status especially for those with chronic disease, but
such records are usually incomplete due to unpunctuality and absence of
patients. In order to resolve the missing data problem over time, tensor-based
model is suggest... | computer science |
3,395 | Initialization and Coordinate Optimization for Multi-way Matching | stat.ML | We consider the problem of consistently matching multiple sets of elements to
each other, which is a common task in fields such as computer vision. To solve
the underlying NP-hard objective, existing methods often relax or approximate
it, but end up with unsatisfying empirical performance due to a misaligned
objective.... | computer science |
3,396 | Adversarial Machine Learning at Scale | cs.CV | Adversarial examples are malicious inputs designed to fool machine learning
models. They often transfer from one model to another, allowing attackers to
mount black box attacks without knowledge of the target model's parameters.
Adversarial training is the process of explicitly training a model on
adversarial examples,... | computer science |
3,397 | Multilinear Low-Rank Tensors on Graphs & Applications | cs.CV | We propose a new framework for the analysis of low-rank tensors which lies at
the intersection of spectral graph theory and signal processing. As a first
step, we present a new graph based low-rank decomposition which approximates
the classical low-rank SVD for matrices and multi-linear SVD for tensors. Then,
building ... | computer science |
3,398 | Deep Variational Inference Without Pixel-Wise Reconstruction | stat.ML | Variational autoencoders (VAEs), that are built upon deep neural networks
have emerged as popular generative models in computer vision. Most of the work
towards improving variational autoencoders has focused mainly on making the
approximations to the posterior flexible and accurate, leading to tremendous
progress. Howe... | computer science |
3,399 | Max-Margin Deep Generative Models for (Semi-)Supervised Learning | cs.CV | Deep generative models (DGMs) are effective on learning multilayered
representations of complex data and performing inference of input data by
exploring the generative ability. However, it is relatively insufficient to
empower the discriminative ability of DGMs on making accurate predictions. This
paper presents max-ma... | computer science |
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