[C2] M2m: Imbalanced Classification via Major-to-minor Translation

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tl;dr: Adversarial examples targeting Majority -> minority can play as surprisingly effective minority samples to prevent overfitting under class-imbalance.

Additional information

Abstract

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.

BibTeX

@InProceedings{kim2020M2m,
  author = {Kim, Jaehyung and Jeong, Jongheon and Shin, Jinwoo},
  title = {M2m: Imbalanced Classification via Major-to-Minor Translation},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  month = {June},
  year = {2020}
}