New Preprint Proposes Noise-Compensated Sharpness-Aware Minimization for Learning with Corrupted Labels

Global: New Preprint Proposes Noise-Compensated Sharpness-Aware Minimization for Learning with Corrupted Labels

On January 24, 2026, researchers Jiayu Xu and Junbiao Pang released a paper titled “Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning” on the arXiv preprint server. The study introduces a technique called NCSAM that seeks to improve deep‑learning model generalization when training data contain erroneous annotations. It situates its contribution within machine learning, artificial intelligence, and computer‑vision research, and claims empirical superiority over existing state‑of‑the‑art methods.

Theoretical Foundations

The authors develop a theoretical analysis linking the flatness of the loss landscape to the presence of label noise. They argue that, under certain conditions, simulated label noise can enhance both generalization performance and robustness, challenging the prevailing view that noise is solely detrimental.

Method Overview: NCSAM

Building on Sharpness‑Aware Minimization (SAM), the proposed Noise‑Compensated Sharpness‑Aware Minimization (NCSAM) incorporates perturbations designed to counteract the adverse effects of noisy labels. The method modifies the optimization trajectory to maintain flat minima while explicitly compensating for label corruption.

Experimental Evaluation

Extensive experiments were conducted on multiple benchmark datasets spanning image classification and other vision tasks. The results, as reported in the abstract, indicate that NCSAM consistently outperforms prior approaches across diverse evaluation metrics, demonstrating higher test accuracy even when trained on noisy data.

Implications for Noisy Label Learning

If validated, the findings suggest a shift toward leveraging controlled noise during training rather than relying exclusively on label‑correction pipelines. This could simplify preprocessing pipelines and reduce reliance on external annotation cleaning tools.

Future Directions

The authors note that further work may explore scaling NCSAM to larger models and datasets, as well as investigating its interaction with other regularization techniques. Additional theoretical refinement of the flatness‑noise relationship is also anticipated.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.