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.
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.