FGSM adversarial fine-tuning avoids catastrophic overfitting at standard eps=4 and 8 and matches PGD robustness within 1.4% at a quarter of the training time.
Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models
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Are Fast Methods Stable in Adversarially Robust Transfer Learning?
FGSM adversarial fine-tuning avoids catastrophic overfitting at standard eps=4 and 8 and matches PGD robustness within 1.4% at a quarter of the training time.