Improving Variational Autoencoder Estimation from Incomplete Data with
  Mixture Variational Families
Vaidotas Simkus Michael U. Gutmann
We consider the task of estimating variational autoencoders (VAEs) when the
training data is incomplete. We show that missing data increases the complexity
of the model's posterior distribution over the latent variables compared to the
fully-observed case. The increased complexity may adversely affect the fit of
the model due to a mismatch between the variational and model posterior
distributions. We introduce two strategies based on (i) finite
variational-mixture and (ii) imputation-based variational-mixture distributions
to address the increased posterior complexity. Through a comprehensive
evaluation of the proposed approaches, we show that variational mixtures are
effective at improving the accuracy of VAE estimation from incomplete data.