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Found 3 papers, 2 papers with code
Improving Robust Fairness via Balance Adversarial Training
Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem.
Automated Discovery of Adaptive Attacks on Adversarial Defenses
Reliable evaluation of adversarial defenses is a challenging task, currently limited to an expert who manually crafts attacks that exploit the defense's inner workings or approaches based on an ensemble of fixed attacks, none of which may be effective for the specific defense at hand.
Deep Learning for Post-Processing Ensemble Weather Forecasts
Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.
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