M2m: Imbalanced Classification via Major-to-minor Translation
- URL: http://arxiv.org/abs/2004.00431v2
- Date: Sun, 20 Dec 2020 10:48:34 GMT
- Title: M2m: Imbalanced Classification via Major-to-minor Translation
- Authors: Jaehyung Kim, Jongheon Jeong, Jinwoo Shin
- Abstract summary: 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 from more-frequent classes.
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.
- Score: 79.09018382489506
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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