Disentangling factors of variation in deep representations using adversarial training

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016.

Cited by: 261|Bibtex|Views183
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During trai...More

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