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On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

This story is from 2026-09-02. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.01410v1 Announce Type: new Abstract: Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly understood. In this work, we develop a statistical framework for conditional generative augmentation and an…

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  1. 2026-09-02 04:00 · arXiv stat.ML
    On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

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