On the Latent Space of Wasserstein Auto-Encoders
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin · arXiv (Cornell University) · 2018
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. We highlight the potential of WAEs for representation learning with promising results on a benchmark disentanglement task.