Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
This addresses the problem of class imbalance in machine learning for practitioners, offering a novel approach that is incremental in applying adversarial techniques to a known issue.
The paper tackles imbalanced learning by using adversarial examples to adjust decision boundaries, achieving comparable performance to state-of-the-art methods on benchmark datasets with improved accuracy for minority classes and minimal loss on majority classes.
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.