Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation

• URL: http://arxiv.org/abs/2102.07125v1
• Date: Sun, 14 Feb 2021 10:43:13 GMT
• Title: Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation
• Authors: Sourav Mishra and Suresh Sundaram
• Abstract summary: A novel data-efficient approach to transfer the knowledge from a teacher model to a student model is presented. 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.
• Score: 8.09591217280048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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