Generative Adversarial Networks (GANs) are the most popular image generation models that have achieved remarkable progress on various computer vision tasks. However, training instability is still one of the open problems for all GAN-based algorithms. Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures. Different from the above methods, in this paper, a new perspective on stabilizing GANs training is presented. It is found that sometimes the images produced by the generator act like adversarial examples of the discriminator during the training process, which may be part of the reason causing the unstable training of GANs. With this finding, we propose the Direct Adversarial Training (DAT) method to stabilize the training process of GANs. Furthermore, we prove that the DAT method is able to minimize the Lipschitz constant of the discriminator adaptively. The advanced performance of DAT is verified on multiple loss functions, network architectures, hyper-parameters, and datasets. Specifically, DAT achieves significant improvements of 11.5% FID on CIFAR-100 unconditional generation based on SSGAN, 10.5% FID on STL-10 unconditional generation based on SSGAN, and 13.2% FID on LSUN-Bedroom unconditional generation based on SSGAN. Code will be available at https://github.com/iceli1007/DAT-GAN

翻译：Adversarial Networks(GANs)是最受欢迎的形象生成模型,在各种计算机视觉任务方面已经取得了显著的进展,然而,培训不稳定仍然是所有基于GAN的算法的公开问题之一。提出了许多方法来稳定GANs的培训,这些方法的重点分别放在损失功能、正规化和正常化技术、培训算法和模型结构上。与上述方法不同,本文介绍了稳定GANs培训的新观点。发现发电机产生的图像有时类似于培训过程中歧视者的对抗性例子,这可能是造成GANs培训不稳定的原因之一。我们提出了直接的Aversarial培训方法,分别放在了GANs的培训进程、正规化和正规化技术、培训算法和模型结构上。此外,我们证明DAT方法能够最大限度地减少SFIDS的利普西常态常态。DAT的先进表现通过多种损失功能的改进、网络结构、超参数和数据集成,这可能是造成GANSANs培训不稳定的原因之一。具体地说,基于SFIRS-AN%的SAR-ANATs 以SAR5和SARISalimal-100为基础,以SARCM%为基础,以S-SARCAN-AN5以S-AN-SADM 建立以S-100为基础。