Empirical Analysis Of Overfitting And Mode Drop In Gan Training
Empirical Analysis Of Overfitting And Mode Drop In Gan Training
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedure, GANs can overfit and exhibit almost no mode drop. Our results shed light on important characteristics of the GAN training procedure. They …