Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs

Understand

Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs).

• VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function.
• GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data.
• The lack of having explicit probability models in GANs prohibits computation of sample likelihoods in their frameworks and limits their use in statistical inference problems.