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arXiv · 1805.05532

Knowledge Distillation with Adversarial Samples Supporting Decision Boundary

Byeongho Heo·Minsik Lee·Sangdoo Yun·Jin Young Choi
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Abstract

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.

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BibTeXRIS

Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi. 2018-05-15. Knowledge Distillation with Adversarial Samples Supporting Decision Boundary. https://arxiv.org/abs/1805.05532

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