Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations

author: Honglak Lee, Computer Science Department, Stanford University
published: Aug. 26, 2009,   recorded: June 2009,   views: 3593
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Description

There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. 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 which 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 which 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.

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Reviews and comments:

Comment1 Comptrol, November 3, 2012 at 12:02 p.m.:

Great presentation, with a bit excitement on the presenter's voice :) I will definitely read all the referenced papers.

Thanks Honglak Lee!

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