NoIsy Label CorrEction

Noisy label correction addresses the pervasive problem of inaccurate labels in training datasets, aiming to improve the robustness and generalization of machine learning models. Current research focuses on developing algorithms that identify and correct noisy labels, often leveraging techniques like multi-scale feature analysis, contrastive learning, and neighborhood-based estimations, adapting these methods to various model architectures and data modalities (e.g., images, videos). Successfully mitigating noisy labels is crucial for enhancing the reliability and fairness of machine learning models across diverse applications, from medical image analysis to fine-grained classification and federated learning.