Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

ICML 2020 Aditi Raghunathan Sang Michael Xie Fanny Yang John Duchi Percy Liang

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... (read more)

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