Training Generative Adversarial Networks in One Stage

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. Computational analysis and experimental results on several datasets and various network architectures demonstrate that, the proposed one-stage training scheme yields a solid 1.5$\times$ acceleration over conventional training schemes, regardless of the network architectures of the generator and discriminator. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. Our source code will be published soon.