title: Discriminator Rejection Sampling
abstract: We propose a rejection sampling scheme using the discriminator of a GAN to
  approximately correct errors in the GAN generator distribution. We show that
  under quite strict assumptions, this will allow us to recover the data distribution
  exactly. We then examine where those strict assumptions break down and design a
  practical algorithm—called Discriminator Rejection Sampling (DRS)—that can be
  used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of
  Gaussians and on the state of the art SAGAN model. On ImageNet, we train an
  improved baseline that increases the best published Inception Score from 52.52 to
  62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use
  DRS to further improve on this baseline, improving the Inception Score to 76.08
  and the FID to 13.75.
authors: Samaneh Azadi Catherine Olsson Trevor Darrell Ian Goodfellow Augustus Odena
hIndex: Trevor Darrell 133