Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
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.
Keywords:wasserstein auto-encoders, WAE
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.
Enter your feedback below and we'll get back to you as soon as possible.