Rolland, Thomas and Basarab, Adrian and Pellegrini, Thomas
Label-consistent sparse auto-encoders.
(2019)
In: Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS 2019), 1 July 2019 - 4 July 2019 (Toulouse, France).
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 184kB |
Abstract
Auto-encoders (AE) is a particular type of unsupervised neural networks that aim at providing a compact representation of a signal or an image [1]. Such AEs are useful for data compression but most of the time the representations they provide are not appropriate as is for a downstream classification task. This is due to the fact that they are trained to minimize a reconstruction error and not a classification loss. Classification attempts with AEs have already been proposed such as contractive AEs [2], correspondence AEs [3] and stacked similarity-aware AEs [4], for instance. Inspired by label-consistent K-SVD (LC-KSVD) [5], we propose a novel supervised version of AEs that integrates class information within the encoded representations.
Item Type: | Conference or Workshop Item (Paper) |
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HAL Id: | hal-02419439 |
Audience (conference): | International conference proceedings |
Uncontrolled Keywords: | |
Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Other partners > Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento - INESC-ID (PORTUGAL) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) |
Laboratory name: | |
Funders: | AMIES : Agence pour les Mathématiques en Interaction avec l'Entreprise et la Société (France) - ANR : Agence nationale de la recherche (France) |
Statistics: | download |
Deposited On: | 06 Dec 2019 15:33 |
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