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Variational methods that rely on a recognition network to approximate theposterior of directed graphical models offer better inference and learning thanprevious methods. Recent advances that exploit the capacity and flexibility inthis approach have expanded what kinds of models can be trained. However, as aproposal for the posterior, the capacity of the recognition network is limited,which can constrain the representational power of the generative model andincrease the variance of Monte Carlo estimates. To address these issues, weintroduce an iterative refinement procedure for improving the approximateposterior of the recognition network and show that training with the refinedposterior is competitive with state-of-the-art methods. The advantages ofrefinement are further evident in an increased effective sample size, whichimplies a lower variance of gradient estimates.

Iterative Refinement of the Approximate Posterior for Directed Belief Networks