Adversarial robustness has become a central goal in deep learning, both in
the theory and the practice. However, successful methods to improve the
adversarial robustness (such as adversarial training) greatly hurt
generalization performance on the unperturbed data. This could have a major
impact on how the adversarial robustness affects real world systems (i.e. many
may opt to forego robustness if it can improve accuracy on the unperturbed
data). We propose Interpolated Adversarial Training, which employs recently
proposed interpolation based training methods in the framework of adversarial
training. On CIFAR-10, adversarial training increases the standard test error
(when there is no adversary) from 4.43% to 12.32%, whereas with our
Interpolated adversarial training we retain the adversarial robustness while
achieving a standard test error of only 6.45%. With our technique, the relative
increase in the standard error for the robust model is reduced from 178.1% to
just 45.5%. Moreover, we provide mathematical analysis of Interpolated
Adversarial Training to confirm its efficiencies and demonstrate its advantages
in terms of robustness and generalization.

本论文提出了一种名为 Interpolated Adversarial Training 的对抗性训练方法，它采用最近提出的基于插值的训练方法来提高对抗鲁棒性，并以 CIFAR-10 数据集为例进行了实验证明，相较传统对抗性训练方法，该方法可以在保持对抗鲁棒性的同时取得更好的泛化性能与更低的标准测试误差。