We propose a novel knowledge distillation approach to facilitate the transfer of dark knowledge from a teacher to a student. Contrary to most of the existing methods that rely on effective training of student models given pretrained teachers, we aim to learn the teacher models that are friendly to students and, consequently, more appropriate for knowledge transfer. In other words, even at the time of optimizing a teacher model, the proposed algorithm learns the student branches jointly to obtain student-friendly representations. Since the main goal of our approach lies in training teacher models and the subsequent knowledge distillation procedure is straightforward, most of the existing knowledge distillation algorithms can adopt this technique to improve the performance of the student models in terms of accuracy and convergence speed. The proposed algorithm demonstrates outstanding accuracy in several well-known knowledge distillation techniques with various combinations of teacher and student architectures.

本文提出一种新颖的知识蒸馏方法，该方法旨在学习对学生友好的教师模型以实现知识传递，并在多个知识蒸馏技术中展示出卓越的准确性和收敛速度。

针对知识蒸馏的学生友好型教师网络学习