On Unifying Deep Generative Models

Understand

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.