  Training of Generative Adversarial Networks (GANs) is notoriously fragile,
requiring to maintain a careful balance between the generator and the
discriminator in order to perform well. To mitigate this issue we introduce a
new regularization technique - progressive augmentation of GANs (PA-GAN). The
key idea is to gradually increase the task difficulty of the discriminator by
progressively augmenting its input or feature space, thus enabling continuous
learning of the generator. We show that the proposed progressive augmentation
preserves the original GAN objective, does not compromise the discriminator's
optimality and encourages a healthy competition between the generator and
discriminator, leading to the better-performing generator. We experimentally
demonstrate the effectiveness of PA-GAN across different architectures and on
multiple benchmarks for the image synthesis task, on average achieving ~3 point
improvement of the FID score.
