DDG dynamically adjusts perturbation magnitude and supervision strength in fast adversarial training according to sample confidence at the ground-truth class, mitigating catastrophic overfitting and the robustness-accuracy trade-off.
Fast adversarial training with noise augmentation: A unified perspective on randstart and gradalign
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Mitigating Error Amplification in Fast Adversarial Training
DDG dynamically adjusts perturbation magnitude and supervision strength in fast adversarial training according to sample confidence at the ground-truth class, mitigating catastrophic overfitting and the robustness-accuracy trade-off.