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Despite the fact that adversarial training has become the de facto method forimproving the robustness of deep neural networks, it is well-known that vanillaadversarial training suffers from daunting robust overfitting, resulting inunsatisfactory robust generalization. A number of approaches have been proposedto address these drawbacks such as extra regularization, adversarial weightsperturbation, and training with more data over the last few years. However, therobust generalization improvement is yet far from satisfactory. In this paper,we approach this challenge with a brand new perspective -- refining historicaloptimization trajectories. We propose a new method named \textbf{WeightedOptimization Trajectories (WOT)} that leverages the optimization trajectoriesof adversarial training in time. We have conducted extensive experiments todemonstrate the effectiveness of WOT under various state-of-the-art adversarialattacks. Our results show that WOT integrates seamlessly with the existingadversarial training methods and consistently overcomes the robust overfittingissue, resulting in better adversarial robustness. For example, WOT boosts therobust accuracy of AT-PGD under AA-$L_{\infty}$ attack by 1.53\% $\sim$ 6.11\%and meanwhile increases the clean accuracy by 0.55\%$\sim$5.47\% across SVHN,CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets.

Enhancing Adversarial Training via Reweighting Optimization Trajectory