Label Refinement

Label refinement focuses on improving the accuracy of initially imperfect or noisy labels in machine learning datasets, ultimately enhancing model performance and robustness. Current research emphasizes techniques like active learning, pseudo-label optimization (often involving threshold adjustment and refinement networks), and contrastive methods to iteratively refine labels, particularly in challenging scenarios such as imbalanced datasets and unsupervised learning. These advancements are significant because they address the limitations of relying solely on initially noisy or incomplete labels, leading to more reliable and accurate models across various applications, including medical image analysis, object detection, and person re-identification.