  We focus on generative autoencoders, such as variational or adversarial
autoencoders, which jointly learn a generative model alongside an inference
model. Generative autoencoders are those which are trained to softly enforce a
prior on the latent distribution learned by the inference model. We call the
distribution to which the inference model maps observed samples, the learned
latent distribution, which may not be consistent with the prior. We formulate a
Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively
decoding and encoding, which allows us to sample from the learned latent
distribution. Since, the generative model learns to map from the learned latent
distribution, rather than the prior, we may use MCMC to improve the quality of
samples drawn from the generative model, especially when the learned latent
distribution is far from the prior. Using MCMC sampling, we are able to reveal
previously unseen differences between generative autoencoders trained either
with or without a denoising criterion.
