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arXiv · 1810.02789

Doubly Semi-Implicit Variational Inference

Dmitry Molchanov·Valery Kharitonov·Artem Sobolev·Dmitry Vetrov
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Abstract

We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference in models where the prior and the posterior can be expressed as an intractable infinite mixture of some analytic density with a highly flexible implicit mixing distribution. We provide a sandwich bound on the evidence lower bound (ELBO) objective that can be made arbitrarily tight. Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact. We evaluate DSIVI on a set of problems that benefit from implicit priors. In particular, we show that DSIVI gives rise to a simple modification of VampPrior, the current state-of-the-art prior for variational autoencoders, which improves its performance.

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Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov. 2018-10-05. Doubly Semi-Implicit Variational Inference. https://arxiv.org/abs/1810.02789

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