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.

翻译：训练生成对抗网络（GANs）仍是一项具有挑战性的问题。判别器通过学习真实/生成数据的分布来训练生成器。然而，生成数据的分布在整个训练过程中不断变化，这使得判别器难以有效学习。本文从在线持续学习的视角提出了一种新颖的GAN方法。我们观察到，基于历史生成数据训练的判别器模型，往往在适应新生成数据的分布变化时表现迟缓，从而降低了生成结果的质量。通过将训练中的生成数据视为数据流，我们提出检测判别器是否在学习新知识方面出现滞后现象，从而显式地强制判别器快速获取新知识。具体而言，我们设计了一种新型判别器：它能自动检测自身的学习滞后程度，并动态掩蔽其网络特征，使判别器能够自适应地学习生成数据随时间变化的分布。实验结果表明，我们的方法优于当前最先进的技术方案。