Knowledge distillation (KD) is a technique to derive optimal performance from
a small student network (SN) by distilling knowledge of a large teacher network
(TN) and transferring the distilled knowledge to the small SN. Since a role of
convolutional neural network (CNN) in KD is to embed a dataset so as to perform
a given task well, it is very important to acquire knowledge that considers
intra-data relations. Conventional KD methods have concentrated on distilling
knowledge in data units. To our knowledge, any KD methods for distilling
information in dataset units have not yet been proposed. Therefore, this paper
proposes a novel method that enables distillation of dataset-based knowledge
from the TN using an attention network. The knowledge of the embedding
procedure of the TN is distilled to graph by multi-head attention (MHA), and
multi-task learning is performed to give relational inductive bias to the SN.
The MHA can provide clear information about the source dataset, which can
greatly improves the performance of the SN. Experimental results show that the
proposed method is 7.05% higher than the SN alone for CIFAR100, which is 2.46%
higher than the state-of-the-art.

本文提出了一种新颖的知识蒸馏方法，通过多头注意力网络从大型教师网络中提取基于数据集的知识，并进行多任务学习，大大提高了小型学生网络的性能。