  Generative Adversarial Networks (GANs) are a class of generative models used
for various applications, but they have been known to suffer from the mode
collapse problem, in which some modes of the target distribution are ignored by
the generator. Investigative study using a new data generation procedure
indicates that the mode collapse of the generator is driven by the
discriminator's inability to maintain classification accuracy on previously
seen samples, a phenomenon called Catastrophic Forgetting in continual
learning. Motivated by this observation, we introduce a novel training
procedure that adaptively spawns additional discriminators to remember previous
modes of generation. On several datasets, we show that our training scheme can
be plugged-in to existing GAN frameworks to mitigate mode collapse and improve
standard metrics for GAN evaluation.
