arXiv:2601.19947v2 Announce Type: replace Abstract: Learning from Noisy Labels (LNL) remains a fundamental challenge in deep learning because real-world datasets often contain corrupted annotations. Most existing methods rely on label correction or sample selection mechanisms. In contrast, we study LNL from an optimization perspective by establishing a theoretical connection between label noise and the flatness-seeking behavior of Sharpness-Aware Minimization (SAM). Based on this analysis, we propose Noise-Compensated Sharpness-Aware Minimization (NCSAM), which uses a noise-compensated perturb
The proliferation of real-world datasets with inherent label noise necessitates robust deep learning methods, driving current research into more resilient optimization techniques.
This development offers a more efficient and mathematically grounded approach to mitigating noisy labels, potentially improving the reliability and performance of AI models trained on imperfect data.
Deep learning models can now be trained more effectively on datasets with corrupted annotations, reducing the need for extensive manual data cleaning or complex label correction mechanisms.
- · AI researchers
- · Deep learning practitioners
- · Companies with large, noisy datasets
- · Industries relying on sensor data
- · Platforms solely focused on manual data annotation
- · Inflexible deep learning frameworks
Improved accuracy and robustness of deep learning systems in real-world applications.
Faster development cycles for AI models as data preparation becomes less onerous and error-prone.
Broader applicability of AI in domains where precise, clean labels are prohibitively expensive or impossible to obtain.
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Read at arXiv cs.LG