Wasserstein Auto-Encoders: Latent Dimensionality and Random EncodersDownload PDF

12 Feb 2018 (modified: 05 May 2023)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.
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.
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