  Knowledge distillation (KD) aims at improving the performance of a compact
student model by distilling the knowledge from a high-performing teacher model.
In this paper, we present an adaptive KD approach, namely AdaDistill, for deep
face recognition. The proposed AdaDistill embeds the KD concept into the
softmax loss by training the student using a margin penalty softmax loss with
distilled class centers from the teacher. Being aware of the relatively low
capacity of the compact student model, we propose to distill less complex
knowledge at an early stage of training and more complex one at a later stage
of training. This relative adjustment of the distilled knowledge is controlled
by the progression of the learning capability of the student over the training
iterations without the need to tune any hyper-parameters. Extensive experiments
and ablation studies show that AdaDistill can enhance the discriminative
learning capability of the student and demonstrate superiority over various
state-of-the-art competitors on several challenging benchmarks, such as IJB-B,
IJB-C, and ICCV2021-MFR
