SOURCE-LINKED INTELLIGENCE
On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
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 analyze its impact on classification risk. We formalize augmentation as a distribution-mixing process and show that the resulting risk distortion is controlled by both the augmentation strength and the class-conditional Wasserstein discrepancy between real and generated distributions. We further derive a capacity-dependent generalization bound based
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- arXiv · AI, language, vision and robotics · 2026-09-01T15:30:24.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.