In most real-world scenarios, labeled 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 by augmenting less-frequent classes via translating
samples (e.g., images) from more-frequent classes. This simple approach enables
a classifier to learn more generalizable features of minority classes, by
transferring and leveraging the diversity of the majority information. Our
experimental results on a variety of class-imbalanced datasets show that the
proposed method improves the generalization on minority classes significantly
compared to other existing re-sampling or re-weighting methods. The performance
of our method even surpasses those of previous state-of-the-art methods for the
imbalanced classification.

通过将更频繁出现的类别样本进行翻译，从而扩充较少出现的类别，这种简单的方法使分类器能够学习到少数类别更具有泛化性的特征，从而提高了在不平衡分类问题上的性能。实验结果表明，该方法在各种不平衡数据集上均优于现有的重取样或重加权方法，甚至超过了之前的最优方法。