Label Correction

Label correction addresses the pervasive problem of inaccurate or noisy labels in datasets, aiming to improve the accuracy and reliability of machine learning models. Current research focuses on developing algorithms that identify and correct noisy labels using techniques like graph neural networks, meta-learning, and ensemble methods, often integrated within end-to-end training frameworks. These advancements are crucial for improving the performance of models trained on real-world data, which frequently contain annotation errors, and have significant implications for various applications, including medical image analysis, semantic segmentation, and federated learning.