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.
On the vulnerability of deep reinforcement learning to backdoor attacks in autonomous vehicles
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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.