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）仍然是一个具有挑战性的问题。判别器通过学习真实/生成数据的分布来训练生成器。然而，在整个训练过程中，生成数据的分布会发生变化，这使得判别器难以学习。本文从在线持续学习的视角提出了一种全新的GAN方法。我们观察到，基于历史生成数据训练的判别器模型，其对新到达生成数据变化的适应速度往往会减慢，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生成数据的新知识方面出现滞后。因此，我们可以明确强制判别器快速学习新知识。具体而言，我们提出了一种新型判别器，它能自动检测自身的滞后状态，并动态掩码其特征，从而使判别器能够自适应地学习生成数据的时变分布。实验结果表明，我们的方法优于当前最先进的方法。