  Many recent works on knowledge distillation have provided ways to transfer
the knowledge of a trained network for improving the learning process of a new
one, but finding a good technique for knowledge distillation is still an open
problem. In this paper, we provide a new perspective based on a decision
boundary, which is one of the most important component of a classifier. The
generalization performance of a classifier is closely related to the adequacy
of its decision boundary, so a good classifier bears a good decision boundary.
Therefore, transferring information closely related to the decision boundary
can be a good attempt for knowledge distillation. To realize this goal, we
utilize an adversarial attack to discover samples supporting a decision
boundary. Based on this idea, to transfer more accurate information about the
decision boundary, the proposed algorithm trains a student classifier based on
the adversarial samples supporting the decision boundary. Experiments show that
the proposed method indeed improves knowledge distillation and achieves the
state-of-the-arts performance.
