Learning features by contrasting natural images with noise

M. Gutmann, A. Hyvärinen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modeling the statistical structure of natural images is interesting for reasons related to neuroscience as well as engineering. Currently, this modeling relies heavily on generative probabilistic models. The estimation of such models is, however, difficult, especially when they consist of multiple layers. If the goal lies only in estimating the features, i.e. in pinpointing structure in natural images, one could also estimate instead a discriminative probabilistic model where multiple layers are more easily handled. For that purpose, we propose to estimate a classifier that can tell natural images apart from reference data which has been constructed to contain some known structure of natural images. The features of the classifier then reveal the interesting structure. Here, we use a classifier with one layer of features and reference data which contains the covariance-structure of natural images. We show that the features of the classifier are similar to those which are obtained from generative probabilistic models. Furthermore, we investigate the optimal shape of the nonlinearity that is used within the classifier.
Original languageEnglish
Title of host publicationArtificial Neural Networks – ICANN 2009
Subtitle of host publication19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II
EditorsC. Alippi, M. Polycarpou, C. Panayiotou, G. Ellinas
PublisherSpringer Berlin Heidelberg
Pages623-632
Number of pages10
ISBN (Electronic)978-3-642-04277-5
ISBN (Print)978-3-642-04276-8
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume5769
ISSN (Print)0302-9743

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