  Adversarial training has been demonstrated to be the most effective approach
to defend against adversarial attacks. However, existing adversarial training
methods show apparent oscillations and overfitting issue in the training
process, degrading the defense efficacy. In this work, we propose a novel
framework, termed Parameter Interpolation based Adversarial Training (PIAT),
that makes full use of the historical information during training.
Specifically, at the end of each epoch, PIAT tunes the model parameters as the
interpolation of the parameters of the previous and current epochs. Besides, we
suggest to use the Normalized Mean Square Error (NMSE) to further improve the
robustness by aligning the clean and adversarial examples. Compared with other
regularization methods, NMSE focuses more on the relative magnitude of the
logits rather than the absolute magnitude. Extensive experiments on several
benchmark datasets and various networks show that our method could prominently
improve the model robustness and reduce the generalization error. Moreover, our
framework is general and could further boost the robust accuracy when combined
with other adversarial training methods.
