  This paper aims to provide a selective survey about knowledge
distillation(KD) framework for researchers and practitioners to take advantage
of it for developing new optimized models in the deep neural network field. To
this end, we give a brief overview of knowledge distillation and some related
works including learning using privileged information(LUPI) and generalized
distillation(GD). Even though knowledge distillation based on the
teacher-student architecture was initially devised as a model compression
technique, it has found versatile applications over various frameworks.
  In this paper, we review the characteristics of knowledge distillation from
the hypothesis that the three important ingredients of knowledge distillation
are distilled knowledge and loss,teacher-student paradigm, and the distillation
process. In addition, we survey the versatility of the knowledge distillation
by studying its direct applications and its usage in combination with other
deep learning paradigms. Finally we present some future works in knowledge
distillation including explainable knowledge distillation where the analytical
analysis of the performance gain is studied and the self-supervised learning
which is a hot research topic in deep learning community.
