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• W3014222654 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." @default.
• W3014222654 created "2020-04-10" @default.
• W3014222654 creator A5002819499 @default.
• W3014222654 creator A5035397475 @default.
• W3014222654 creator A5045815823 @default.
• W3014222654 date "2020-04-01" @default.
• W3014222654 modified "2023-10-16" @default.
• W3014222654 title "M2m: Imbalanced Classification via Major-to-minor Translation" @default.
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• W3014222654 doi "https://doi.org/10.48550/arxiv.2004.00431" @default.
• W3014222654 hasPublicationYear "2020" @default.
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