2009 ConvolutionalDeepBeliefNetworks

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Subject Headings: Convolutional Neural Network.

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Abstract

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 object images data 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.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 ConvolutionalDeepBeliefNetworksHonglak Lee
Roger Grosse
Rajesh Ranganath
Andrew Y. Ng
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations10.1145/1553374.15534532009
AuthorHonglak Lee +, Roger Grosse +, Rajesh Ranganath + and Andrew Y. Ng +
doi10.1145/1553374.1553453 +
titleConvolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations +
year2009 +