  Variational Autoencoder is a scalable method for learning latent variable
models of complex data. It employs a clear objective that can be easily
optimized. However, it does not explicitly measure the quality of learned
representations. We propose a Variational Mutual Information Maximization
Framework for VAE to address this issue. It provides an objective that
maximizes the mutual information between latent codes and observations. The
objective acts as a regularizer that forces VAE to not ignore the latent code
and allows one to select particular components of it to be most informative
with respect to the observations. On top of that, the proposed framework
provides a way to evaluate mutual information between latent codes and
observations for a fixed VAE model.
