Imbalanced Classification via Adversarial Minority Over-sampling
In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes.Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information.Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.
We develop a new method for imbalanced classification using adversarial examplesProposes a new optimization objective that generates synthetic samples by over-sampling the majority classes instead of minority classes, solving the problem of overfitting minority classes.The authors propose to tackle imbalance classification using re-sampling methods, showing that adversarial examples in the minority class would help to train a new model that generalizes better.