Understanding Data Augmentation under Label Noise
Data augmentation (DA) has become a fundamental component of modern machine learning systems and is widely regarded as an effective strategy for improving robustness and generalization. Despite its empirical success, the theoretical impact of DA under label noise remains poorly understood. In this work, we first theoretically explain when DA is beneficial and when it becomes detrimental, and provide sufficient conditions for distinguishing between these two regimes under both the i.i.d. learning setting and the more challenging domain generalization scenario. Then, we reveal a counterintuitive phenomenon: DA essentially acts as an uncertainty amplifier in the learning process, decreasing ...
| Authors | Hong Zheng, Fei Teng |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 21 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.