Reviews: Progressive Augmentation of GANs

This paper introduces a novel regularization method (e.g. Instead of weakening or regularizing the discriminator, the idea is to augment the data samples or features with random bits to increase the discrimination task difficulty. In this way, it could prevent the discriminator from being overconfident and maintain a healthy competition, which would enable the generator to be continuously optimized. The augmentation could be progressively levelled up during the training by evaluating the kernel inception distance between synthetic samples and training data samples. The proposed method has been demonstrated on different datasets and compared with other regularization techniques.