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.

本文研究了对抗样本在不平衡学习中的应用，在训练中引入 Guiding Adversarial Examples (GAEs) 方法，通过调整有偏的决策边界，将该方法应用在少数派类别样本的分类任务中，证明其能有效提高少数派类别样本的准确率，而对大多数派样本的准确率影响较小，该方法可与现有最先进方法媲美。据我们所知，我们是第一个应用对抗样本处理不平衡学习问题的研究。