  Knowledge transfer between artificial neural networks has become an important
topic in deep learning. Among the open questions are what kind of knowledge
needs to be preserved for the transfer, and how it can be effectively achieved.
Several recent work have shown good performance of distillation methods using
relation-based knowledge. These algorithms are extremely attractive in that
they are based on simple inter-sample similarities. Nevertheless, a proper
metric of affinity and use of it in this context is far from well understood.
In this paper, by explicitly modularising knowledge distillation into a
framework of three components, i.e. affinity, normalisation, and loss, we give
a unified treatment of these algorithms as well as study a number of unexplored
combinations of the modules. With this framework we perform extensive
evaluations of numerous distillation objectives for image classification, and
obtain a few useful insights for effective design choices while demonstrating
how relation-based knowledge distillation could achieve comparable performance
to the state of the art in spite of the simplicity.
