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In most real-world scenarios, labeled training datasets are highlyclass-imbalanced, where deep neural networks suffer from generalizing to abalanced testing criterion. In this paper, we explore a novel yet simple way toalleviate this issue by augmenting less-frequent classes via translatingsamples (e.g., images) from more-frequent classes. This simple approach enablesa classifier to learn more generalizable features of minority classes, bytransferring and leveraging the diversity of the majority information. Ourexperimental results on a variety of class-imbalanced datasets show that theproposed method improves the generalization on minority classes significantlycompared to other existing re-sampling or re-weighting methods. The performanceof our method even surpasses those of previous state-of-the-art methods for theimbalanced classification.

M2m: Imbalanced Classification via Major-to-minor Translation