Knowledge Distillation (KD) based methods adopt the one-way Knowledge
Transfer (KT) scheme in which training a lower-capacity student network is
guided by a pre-trained high-capacity teacher network. Recently, Deep Mutual
Learning (DML) presented a two-way KT strategy, showing that the student
network can be also helpful to improve the teacher network. In this paper, we
propose Dense Cross-layer Mutual-distillation (DCM), an improved two-way KT
method in which the teacher and student networks are trained collaboratively
from scratch. To augment knowledge representation learning, well-designed
auxiliary classifiers are added to certain hidden layers of both teacher and
student networks. To boost KT performance, we introduce dense bidirectional KD
operations between the layers appended with classifiers. After training, all
auxiliary classifiers are discarded, and thus there are no extra parameters
introduced to final models. We test our method on a variety of KT tasks,
showing its superiorities over related methods. Code is available at
this https URL

在这篇论文中，我们提出了一种改进的双向知识迁移方法 Dense Cross-layer Mutual-distillation (DCM)，它采用了协作训练师生网络的方法，引入了辅助分类器和密集的双向知识蒸馏操作，在各种知识迁移任务中都表现出比相关方法更卓越的性能。