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Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
Abstract:We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders.
TL;DR:We study the role of latent dimensionality in Wasserstein Auto-Encoders, and show that random encoders may often be preferable to deterministic encoders.
Keywords:wasserstein auto-encoders, WAE
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