Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation

Existing methods for distillation use the conventional training approach where all samples participate equally in the process and are thus highly inefficient in terms of data utilization. In this paper, a novel data-efficient approach to transfer the knowledge from a teacher model to a student model is presented. Here, the teacher model uses self-regulation to select appropriate samples for training and identifies their significance in the process. During distillation, the significance information can be used along with the soft-targets to supervise the students. Depending on the use of self-regulation and sample significance information in supervising the knowledge transfer process, three types of distillations are proposed - significance-based, regulated, and hybrid, respectively. Experiments on benchmark datasets show that the proposed methods achieve similar performance as other state-of-the-art methods for knowledge distillation while utilizing a significantly less number of samples.