Triangular Trade-off

Trade-off Between Accuracy, Robustness, and Fairness of Deep Classifiers

Deep classifiers trained on balanced datasets exhibit a class-wise imbalance, which is even more severe for adversarially trained models. We propose a class-wise loss re-weighting to obtain more fair standard and robust classifiers. The final results suggest, that there exists a triangular trade-off between accuracy, robustness, and fairness.