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Deep Micro-Dictionary Learning and Coding Network

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Dictionary learning for fast classification based on soft-thresholding

Pascal Frossard, Alhussein Fawzi

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Deep Neural Network Posterior Features

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

Statistical speech recognition has been cast as a natural realization of the compressive sensing and sparse recovery. The compressed acoustic observations are sub-word posterior probabilities obtained from a deep neural network (DNN). Dictionary learning a ...

2015

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Multi-Graph Learning of Spectral Graph Dictionaries

Pascal Frossard

We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that pro ...

2015

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Bugra Tekin, Roberto Rigamonti, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015

Dictionary Learning Over Distributed Models

Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over ...

IEEE2015

Transformation-Invariant Dictionary Learning for Classification with 1-Sparse Representations

Pascal Frossard, Elif Vural, Ahmet Caner Yüzügüler

Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the de ...

2014

Learning Parametric Dictionaries for Signals on Graphs

Pascal Frossard, David Shuman

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on wei ...

Institute of Electrical and Electronics Engineers2014

Online dictionary learning over distributed models

Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in big data scenarios where multiple large dictionary models may be spr ...

IEEE2014

Learning non-parametric basis independent models from point queries via low-rank methods

Volkan Cevher, Hemant Tyagi

We consider the problem of learning multi-ridge functions of the form f (x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l(2)-ball in R-d, g is twice continuously differentiable almost everywhere, and A is an element ...

Elsevier2014