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Understanding and Mitigating the Tradeoff Between Robustness and Accuracy
[article]
2020
arXiv
pre-print
Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). Previous explanations for this tradeoff rely on the assumption that no predictor in the hypothesis class has low standard and robust error. In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor
arXiv:2002.10716v2
fatcat:mq6fvnellfegzax3sohbeeitpu