Title: Distribution Matching in Variational Inference,
Abstract: We show that Variational Autoencoders consistently fail to learn marginal
distributions in latent and visible space. We ask whether this is a consequence
of matching conditional distributions, or a limitation of explicit model and
posterior distributions. We explore alternatives provided by marginal
distribution matching and implicit distributions through the use of Generative
Adversarial Networks in variational inference. We perform a large-scale
evaluation of several VAE-GAN hybrids and explore the implications of class
probability estimation for learning distributions. We conclude that at present
VAE-GAN hybrids have limited applicability: they are harder to scale, evaluate,
and use for inference compared to VAEs; and they do not improve over the
generation quality of GANs.