As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our results highlighted increased performance and reduced bias for all our experiments. In addition, we further show that having biased/specialized teachers is crucial by showing that our approach achieves better results than when knowledge is distilled from four teachers trained on balanced datasets. Our approach represents a step forward to the understanding of the importance of ethnicity-specific features.
@article{arxiv.2408.16563,
title = {MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition},
author = {Eduarda Caldeira and Jaime S. Cardoso and Ana F. Sequeira and Pedro C. Neto},
journal= {arXiv preprint arXiv:2408.16563},
year = {2024}
}