Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

ICLR, Volume abs/1807.07543, 2019.

Cited by: 76|Bibtex|Views187
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which se...More

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