Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation. 
 However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasserstein objective. It is smoother and more stable to optimize. In this paper, we show that the Wasserstein distance is just one out of a large family of objective functions that yield these properties. By making the discriminator of a GAN robust to adversarial attacks we can turn any GAN objective into a smooth and stable loss. We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively. The training additionally becomes more robust to suboptimal choices of hyperparameters, model architectures, or objective functions. Generative adversarial networks (GANs) BID4 are at the forefront of generative modeling. They cast generative modeling as a never ending duel between two networks: a generator network produces synthetic samples from random noise and a discriminator network distinguishes synthetic from real data samples. GANs produce visually appealing samples, but are often hard to train. Much recent work, tries to stabilize GAN training through novel architecture BID25 BID3 or training objectives BID17 BID6 .In this paper, we show that GAN objectives significantly stabilize, if the discriminator is robust to adversarial attacks. Specifically , we show that a robust discriminator leads to a robust minimax objective for the generator irrespective of the training objective used. In addition , any training objective is smooth and Lipschitz as long as the discriminator is Lipschitz. Finally, we show that this robustness does not need to be enforced for every single input of the discriminator, but rather just in expectation over the generated samples. We present two new regularization terms, borrowed from the adversarial training literature. Both terms ensure the robustness of the discriminator. They are easy to optimize and can be added to any existing GAN objective.Our experiments show that adversarial robustness both improves the visual quality of the results, as well as stabilizes the training procedure across a wide range of architectures, hyper-parameters and training objectives. We will publish the code and data used to perform our experiments upon acceptance. In this paper, we established a clear connection between robust discriminators in generative adversarial networks and the overall smoothness of the optimization and the quality of the results. To our knowledge, we are the first to show that a robustness regularization guarantees a smooth and robust loss function for any GAN objective. Finally, our results suggest that robust regularization leads to better training and visual results than standard gradient penalties.
A discriminator that is not easily fooled by adversarial example makes GAN training more robust and leads to a smoother objective. This paper proposes a new way to stabilize the training process of GAN by regularizing the Discriminator to be robust to adversarial examples. 
The paper proposes a systematic way of training GANs with robustness regularization terms, allowing for smoother training of GANs.  Presents idea that making a discriminator robust to adversarial perturbations the GAN objective can be made smooth which results in better results both visually and in terms of FID.