Clean Sample

Identifying "clean samples"—data points with accurate labels—within noisy datasets is crucial for robust machine learning model training. Current research focuses on developing algorithms that effectively separate clean from noisy data, employing techniques like penalized regression, vision-language models (e.g., CLIP), and diffusion models to achieve this separation. These methods aim to improve model generalization and performance by mitigating the negative impact of noisy labels, ultimately leading to more reliable and accurate machine learning applications. The development of efficient and theoretically sound clean sample selection methods is a significant area of ongoing investigation.