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.
Zerograd: Mitigating and explaining catastrophic overfitting in fgsm adversarial training
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Catastrophic overfitting in fast adversarial training is reinterpreted as a weak-trigger variant of unlearnable tasks, allowing backdoor-inspired recalibration and outlier suppression to restore robustness.
An adaptive l^p norm control in FGSM adversarial training, guided by participation ratio and entropy of gradients, mitigates catastrophic overfitting without noise or regularization.
citing papers explorer
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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.
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Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Catastrophic overfitting in fast adversarial training is reinterpreted as a weak-trigger variant of unlearnable tasks, allowing backdoor-inspired recalibration and outlier suppression to restore robustness.
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Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless $l^p$ Norm Solution for Fast Adversarial Training
An adaptive l^p norm control in FGSM adversarial training, guided by participation ratio and entropy of gradients, mitigates catastrophic overfitting without noise or regularization.