Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Examples for Good: Adversarial Examples Guided Imbalanced
  Learning   Adversarial examples are inputs for machine learning models that have been
designed by attackers to cause the model to make mistakes. In this paper, we
demonstrate that adversarial examples can also be utilized for good to improve
the performance of imbalanced learning. We provide a new perspective on how to
deal with imbalanced data: adjust the biased decision boundary by training with
Guiding Adversarial Examples (GAEs). Our method can effectively increase the
accuracy of minority classes while sacrificing little accuracy on majority
classes. We empirically show, on several benchmark datasets, our proposed
method is comparable to the state-of-the-art method. To our best knowledge, we
are the first to deal with imbalanced learning with adversarial examples.

13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn