Targeted data augmentation for improving model robustness - Mikołajczyk-Bareła, Agnieszka; Ferlin, Maria; Grochowski, Michał (Zielona Góra: Uniwersytet Zielonogórski)
This paper proposes a new and effective bias mitigation method called targeted data augmentation (TDA). Since removing biases is often tedious and challenging and may not always lead to effective bias mitigation, we propose an alternative approach: skillfully inserting biases during the training to improve model robustness. To validate the proposed method, we applied TDA to two representative and diverse datasets: a clinical skin lesion dataset and a dataset of male and female faces.
Keywords: bias mitigation; classification; data augmentation; deep neutral network