Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Knowledge Distillation with Adversarial Samples Supporting Decision
  Boundary   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.

32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio