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.
Stable GAN training in high dimensions by using an array of discriminators, each with a low dimensional view of generated samples The paper proposes to stabilize GAN training by using an ensemble of discriminators, each working on a random projection of the input data, to provide the training signal for the generator model. The paper proposes a GAN training method for improving the training stability.  The paper proposes a new approach to GAN training, which provides stable gradients to train the generator.