Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator

Understand

Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data.

• The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability Metric~(Wasserstein GANs).
• With $f$-divergence as the objective function, the discriminator essentially estimates the density ratio, and the estimated ratio proves useful in further improving the sample quality of the generator.
• However, how to leverage the information contained in the discriminator of Wasserstein GANs (WGAN) is less explored.