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Towards Conceptual Compression

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arxiv 1604.08772 v1 pith:L5PXOH2K submitted 2016-04-29 stat.ML cs.CVcs.LG

Towards Conceptual Compression

Karol Gregor , Frederic Besse , Danilo Jimenez Rezende , Ivo Danihelka , Daan Wierstra This is my paper
classification stat.ML cs.CVcs.LG
keywords conceptualcompressionglobalimageinformationachieveaddressingallows
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We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets. We show that it naturally separates global conceptual information from lower level details, thus addressing one of the fundamentally desired properties of unsupervised learning. Furthermore, the possibility of restricting ourselves to storing only global information about an image allows us to achieve high quality 'conceptual compression'.

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    cs.LG 2016-05 accept novelty 8.0

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