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Found 4 papers, 1 papers with code
On Frank-Wolfe Adversarial Training
We develop a theoretical framework for adversarial training (AT) with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of $\ell_\infty$ FW attacks (the attack's $\ell_2$ norm).
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training
We develop a theoretical framework for adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the $\ell_2$ distortion of $\ell_\infty$ FW attacks.
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic.
Second Order Optimization for Adversarial Robustness and Interpretability
It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT.
