  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.
