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JMLR
2010

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

10 years 8 months ago
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzmann machine (RBM) which is used as a module for learning deep belief nets one layer at a time. The Gaussian-Binary RBMs that have been used to model real-valued data are not a good way to model the covariance structure of natural images. We propose a factored 3-way RBM that uses the states of its hidden units to represent abnormalities in the local covariance structure of an image. This provides a probabilistic framework for the widely used simple/complex cell architecture. Our model learns binary features that work very well for object recognition on the "tiny images" data set. Even better features are obtained by then using standard binary RBM's to learn a deeper model.
Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E.
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton
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