Noisy Label Sample

Noisy label samples, where training data contains incorrect or unreliable labels, pose a significant challenge to the effectiveness of machine learning models. Current research focuses on developing robust algorithms that can identify and mitigate the impact of these noisy labels, often employing techniques like sample selection based on loss values or model consensus, and incorporating semi-supervised learning strategies to leverage both labeled and unlabeled data. Addressing this issue is crucial for improving the reliability and generalizability of machine learning models trained on real-world datasets, particularly in applications with high annotation costs or inherent label ambiguity, such as medical image analysis.