Noisy Sample

Noisy samples, ubiquitous in real-world datasets, pose a significant challenge to machine learning models by degrading their generalization performance. Current research focuses on developing robust methods to identify and mitigate the effects of noisy labels, employing techniques like sample selection, label correction, and data augmentation, often within frameworks incorporating teacher-student models, Gaussian Mixture Models, or K-Nearest Neighbors. These advancements are crucial for improving the reliability and accuracy of machine learning models across diverse applications, from recommendation systems and image recognition to speech processing and medical diagnosis, where noisy data is prevalent.