  Training Generative Adversarial Networks (GANs) remains a challenging
problem. The discriminator trains the generator by learning the distribution of
real/generated data. However, the distribution of generated data changes
throughout the training process, which is difficult for the discriminator to
learn. In this paper, we propose a novel method for GANs from the viewpoint of
online continual learning. We observe that the discriminator model, trained on
historically generated data, often slows down its adaptation to the changes in
the new arrival generated data, which accordingly decreases the quality of
generated results. By treating the generated data in training as a stream, we
propose to detect whether the discriminator slows down the learning of new
knowledge in generated data. Therefore, we can explicitly enforce the
discriminator to learn new knowledge fast. Particularly, we propose a new
discriminator, which automatically detects its retardation and then dynamically
masks its features, such that the discriminator can adaptively learn the
temporally-vary distribution of generated data. Experimental results show our
method outperforms the state-of-the-art approaches.
