  Adversarial Training (AT) is known as an effective approach to enhance the
robustness of deep neural networks. Recently researchers notice that robust
models with AT have good generative ability and can synthesize realistic
images, while the reason behind it is yet under-explored. In this paper, we
demystify this phenomenon by developing a unified probabilistic framework,
called Contrastive Energy-based Models (CEM). On the one hand, we provide the
first probabilistic characterization of AT through a unified understanding of
robustness and generative ability. On the other hand, our unified framework can
be extended to the unsupervised scenario, which interprets unsupervised
contrastive learning as an important sampling of CEM. Based on these, we
propose a principled method to develop adversarial learning and sampling
methods. Experiments show that the sampling methods derived from our framework
improve the sample quality in both supervised and unsupervised learning.
Notably, our unsupervised adversarial sampling method achieves an Inception
score of 9.61 on CIFAR-10, which is superior to previous energy-based models
and comparable to state-of-the-art generative models.
