Title: On Unifying Deep Generative Models,
Abstract: Deep generative models have achieved impressive success in recent years.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
emerging families for generative model learning, have largely been considered
as two distinct paradigms and received extensive independent studies
respectively. This paper aims to establish formal connections between GANs and
VAEs through a new formulation of them. We interpret sample generation in GANs
as performing posterior inference, and show that GANs and VAEs involve
minimizing KL divergences of respective posterior and inference distributions
with opposite directions, extending the two learning phases of classic
wake-sleep algorithm, respectively. The unified view provides a powerful tool
to analyze a diverse set of existing model variants, and enables to transfer
techniques across research lines in a principled way. For example, we apply the
importance weighting method in VAE literatures for improved GAN learning, and
enhance VAEs with an adversarial mechanism that leverages generated samples.
Experiments show generality and effectiveness of the transferred techniques.