  Adversarial training has become one of the most effective methods for
improving robustness of neural networks. However, it often suffers from poor
generalization on both clean and perturbed data. In this paper, we propose a
new algorithm, named Customized Adversarial Training (CAT), which adaptively
customizes the perturbation level and the corresponding label for each training
sample in adversarial training. We show that the proposed algorithm achieves
better clean and robust accuracy than previous adversarial training methods
through extensive experiments.
