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Author Ranganath, Rajesh ♦ Grosse, Roger ♦ Lee, Honglak ♦ Ng, Andrew Y.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks (DBNs); however, scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model that scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique that shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.
Description Affiliation: University of Michigan, Ann Arbor, MI (Lee, Honglak) || Stanford University, Stanford, CA (Ranganath, Rajesh; Ng, Andrew Y.) || CSAIL, Massachusetts Institute of Technology, Cambridge, MA (Grosse, Roger)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 54
Issue Number 10
Page Count 9
Starting Page 95
Ending Page 103


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Source: ACM Digital Library