Knowledge distillation is a method of transferring the knowledge from a
complex deep neural network (DNN) to a smaller and faster DNN, while preserving
its accuracy. Recent variants of knowledge distillation include teaching
assistant distillation, curriculum distillation, mask distillation, and
decoupling distillation, which aim to improve the performance of knowledge
distillation by introducing additional components or by changing the learning
process. Teaching assistant distillation involves an intermediate model called
the teaching assistant, while curriculum distillation follows a curriculum
similar to human education. Mask distillation focuses on transferring the
attention mechanism learned by the teacher, and decoupling distillation
decouples the distillation loss from the task loss. Overall, these variants of
knowledge distillation have shown promising results in improving the
performance of knowledge distillation.

知识蒸馏是一种将深度神经网络的知识转移到更小更快的神经网络中的方法，近期变体包括教学助理蒸馏、课程蒸馏、遮罩蒸馏和解耦蒸馏等，致力于通过引入额外的组件或改变学习过程来提高知识蒸馏的性能。