Note on the equivalence of hierarchical variational models and auxiliary deep generative models

This note compares two recently published machine learning methods for constructing flexible, but tractable families of variational hiddenvariable posteriors. The first method, called hierarchical variational models enriches the inference model with an extra variable, while the other, called auxiliary deep generative models, enriches the generative model instead. We conclude that the two methods are mathematically equivalent.