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.

从在线持续学习的角度出发，提出了一种新颖的 GAN 方法，通过将生成的数据视为流来训练鉴别器模型，并自动检测其阻塞并动态遮罩其特征，使其能够适应临时变化的生成数据分布。实验结果表明，该方法优于现有的最先进方法。