Knowledge distillation, a widely used model compression technique, works on
the basis of transferring knowledge from a cumbersome teacher model to a
lightweight student model. The technique involves jointly optimizing the task
specific and knowledge distillation losses with a weight assigned to them.
Despite these weights playing a crucial role in the performance of the
distillation process, current methods provide equal weight to both losses,
leading to suboptimal performance. In this paper, we propose Adaptive Knowledge
Distillation, a novel technique inspired by curriculum learning to adaptively
weigh the losses at instance level. This technique goes by the notion that
sample difficulty increases with teacher loss. Our method follows a
plug-and-play paradigm that can be applied on top of any task-specific and
distillation objectives. Experiments show that our method performs better than
conventional knowledge distillation method and existing instance-level loss
functions.

本文提出了一种自适应知识蒸馏技术，通过课程学习的启发，以实例级别自适应地加权损失，并实验证明该方法优于传统的知识蒸馏方法和现有的实例级别损失函数。