Title: Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization. Abstract: Adversarial training is a commonly used technique to improve model robustness against adversarial examples. Despite its success as a defense mechanism, adversarial training often fails to generalize well to unperturbed test data. While previous work assumes it is caused by the discrepancy between robust and non-robust features, in this paper, we introduce \emph{Adversarial Masking}, a new hypothesis that this trade-off is caused by different feature maskings applied. Specifically, the rescaling operation in the batch normalization layer, when combined together with ReLU activation, serves as a feature masking layer to select different features for model training. By carefully manipulating different maskings, a well-balanced trade-off can be achieved between model performance on unperturbed and perturbed data. Built upon this hypothesis, we further propose Robust Masking (RobMask),  which constructs unique masking for every specific attack perturbation by learning a set of primary adversarial feature maskings. By incorporating different feature maps after the masking, we can distill better features to help model generalization. Sufficiently, adversarial training can be treated as an effective regularizer to achieve better generalization. Experiments on multiple benchmarks demonstrate that RobMask achieves significant improvement on clean test accuracy compared to strong state-of-the-art baselines.