#label-noise News & Analysis

3 articles tagged with #label-noise. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · 3d ago6/10

🧠

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning

Researchers propose NCSAM, a novel optimization-based approach to learning from noisy labels that theoretically connects label noise to Sharpness-Aware Minimization's behavior. The method uses noise-compensated perturbations to reduce memorization of corrupted annotations while maintaining optimization simplicity, demonstrating competitive performance against existing noisy-label learning methods.

AINeutralarXiv – CS AI · May 126/10

🧠

UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning

Researchers introduce UFO, a framework addressing robust continual graph learning by simultaneously tackling catastrophic forgetting and noisy data supervision in evolving graphs. The method uses flow-based generative modeling to mitigate forgetting and instance-level reliability scoring to handle corrupted labels, demonstrating superior performance across benchmark datasets.

AINeutralarXiv – CS AI · Apr 136/10

🧠

Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise

Researchers introduce VisPrompt, a framework that improves prompt learning for vision-language models by injecting visual semantic information to enhance robustness against label noise. The approach keeps pre-trained models frozen while adding minimal trainable parameters, demonstrating superior performance across seven benchmark datasets under both synthetic and real-world noisy conditions.