Emergent Mind
On the Latent Space of Wasserstein Auto-Encoders
(1802.03761)
Published Feb 11, 2018
in
stat.ML
and
cs.LG
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. We highlight the potential of WAEs for representation learning with promising results on a benchmark disentanglement task.
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