  Variational Autoencoders (VAEs) are expressive latent variable models that
can be used to learn complex probability distributions from training data.
However, the quality of the resulting model crucially relies on the
expressiveness of the inference model. We introduce Adversarial Variational
Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily
expressive inference models. We achieve this by introducing an auxiliary
discriminative network that allows to rephrase the maximum-likelihood-problem
as a two-player game, hence establishing a principled connection between VAEs
and Generative Adversarial Networks (GANs). We show that in the nonparametric
limit our method yields an exact maximum-likelihood assignment for the
parameters of the generative model, as well as the exact posterior distribution
over the latent variables given an observation. Contrary to competing
approaches which combine VAEs with GANs, our approach has a clear theoretical
justification, retains most advantages of standard Variational Autoencoders and
is easy to implement.
