GBMix: Enhancing Fairness by Group-Balanced Mixup
Mixup is a powerful data augmentation strategy that has been shown to improve the generalization and adversarial robustness of machine learning classifiers, particularly in computer vision applications.Despite its simplicity and effectiveness, the impact of Mixup on the fairness of a model has not been thoroughly investigated yet.In this paper, we