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。

基于多头注意力网络的图卷积知识蒸馏

Graph-based Knowledge Distillation by Multi-head Attention Network

Metric learning networks are used to compute image embeddings, which are
widely used in many applications such as image retrieval and face recognition.
In this paper, we propose to use network distillation to efficiently compute
image embeddings with small networks. Network distillation has been
successfully applied to improve image classification, but has hardly been
explored for metric learning. To do so, we propose two new loss functions that
model the communication of a deep teacher network to a small student network.
We evaluate our system in several datasets, including CUB-200-2011, Cars-196,
Stanford Online Products and show that embeddings computed using small student
networks perform significantly better than those computed using standard
networks of similar size. Results on a very compact network (MobileNet-0.25),
which can be used on mobile devices, show that the proposed method can greatly
improve Recall@1 results from 27.5\% to 44.6\%. Furthermore, we investigate
various aspects of distillation for embeddings, including hint and attention
layers, semi-supervised learning and cross quality distillation. (Code is
available at this https URL)

本文提出使用网络蒸馏来高效计算小型网络产生的图像嵌入，通过构建两个新的损失函数模拟深度教师网络向小型学生网络通信的机制，在多个数据集上验证我们的方法，得出小型学生网络计算的嵌入明显优于使用相似大小的标准网络计算的结果。其中在可移动设备上使用的 MobileNet-0.25 产生了 44.6% 的 Recall@1 结果。同时，研究了蒸馏嵌入的各种方面，包括提示和注意力层，半监督学习以及不同质量的蒸馏。