  A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoencoders can lead to inaccurate
amortized inference distributions and, in some cases, improving the objective
provably degrades the inference quality. In addition, it has been observed that
variational autoencoders tend to ignore the latent variables when combined with
a decoding distribution that is too flexible. We again identify the cause in
existing training criteria and propose a new class of objectives (InfoVAE) that
mitigate these problems. We show that our model can significantly improve the
quality of the variational posterior and can make effective use of the latent
features regardless of the flexibility of the decoding distribution. Through
extensive qualitative and quantitative analyses, we demonstrate that our models
outperform competing approaches on multiple performance metrics.
