MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
<|reference_start|>MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition: 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.<|reference_end|>
@article{caldeira2024mst-kd:,
    title={MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair
  Face Recognition},
    author={Eduarda Caldeira, Jaime S. Cardoso, Ana F. Sequeira, Pedro C. Neto},
    journal={arXiv preprint arXiv:2408.16563},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16563},
    primaryClass={cs.CV}
}