  Training generative adversarial networks is unstable in high-dimensions as
the true data distribution tends to be concentrated in a small fraction of the
ambient space. The discriminator is then quickly able to classify nearly all
generated samples as fake, leaving the generator without meaningful gradients
and causing it to deteriorate after a point in training. In this work, we
propose training a single generator simultaneously against an array of
discriminators, each of which looks at a different random low-dimensional
projection of the data. Individual discriminators, now provided with restricted
views of the input, are unable to reject generated samples perfectly and
continue to provide meaningful gradients to the generator throughout training.
Meanwhile, the generator learns to produce samples consistent with the full
data distribution to satisfy all discriminators simultaneously. We demonstrate
the practical utility of this approach experimentally, and show that it is able
to produce image samples with higher quality than traditional training with a
single discriminator.
