  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 SAGAN model,
state-of-the-art in the image generation task at the time of developing this
work. On ImageNet, we train an improved baseline that increases the 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.
