Improving Sampling from Generative Autoencoders with Markov Chains

Understand

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.