274 citations · 423 across the 15 of their papers we have counts for
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stat.ML2018★ 1 cited
An Empirical Study of Generative Models with Encoders
Paul K. Rubenstein, Yunpeng Li, Dominik Roblek
Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inv…
stat.ML2018★ 18 cited
On the Latent Space of Wasserstein Auto-Encoders
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin
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 b…