The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

25 Feb 2020Yifei MinLin ChenAmin Karbasi

Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data... (read more)

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