文件名称:
Conv-DBN for Scalable Unsupervised Learning of Hierarchical Representations
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文件大小: 1mb
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上传时间: 2015-05-21
详细说明： 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 dicult problem. To address this problem, we present the convolu- tional deep belief network, a hierarchical generative model which scales to realistic image sizes. This model is translation-invariant and supports ecient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique which shrin ks 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. ...展开收缩
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