  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.
