Generative adversarial networks (GANs) are a class of machine-learning models that use
adversarial training to generate new samples with the same (potentially very complex)
statistics as the training samples. One major form of training failure, known as mode
collapse, involves the generator failing to reproduce the full diversity of modes in the target
probability distribution. Here, we present an effective model of GAN training, which captures
the learning dynamics by replacing the generator neural network with a collection of …

Generative adversarial networks (GANs) are a family of machine-learning models that use
adversarial training to generate new samples with the same statistics as the training
samples. Mode collapse is a major form of training failure in which the generator fails to
reproduce the full diversity of modes in the training data. Here, we present a simplified
model of GAN training dynamics, which allows us to study the conditions under which mode
collapse occurs. Our effective model replaces the generator neural network with a collection …