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.

本研究解决了面部识别系统在不同种族数据上表现不均的问题。我们提出了一种多元专业教师框架，通过训练针对单一种族的四位教师，在一个共同空间中提炼知识，从而提升学生网络的性能并减少偏见。实验结果表明，采用具有偏见的教师显著优于从平衡数据集训练的教师获取的知识。

多元专业教师知识蒸馏用于公平的面部识别