Meta Label Correction

Meta label correction addresses the pervasive problem of noisy or inaccurate labels in machine learning datasets, aiming to improve model accuracy and robustness by refining these labels during training. Current research focuses on developing efficient meta-learning algorithms, often employing neural network architectures with novel training objectives and data purification strategies, to iteratively correct labels using small, clean datasets or inherent data relationships. This work is significant because it enhances the reliability of models trained on real-world data, which frequently contain label imperfections, leading to improved performance in various applications like sentiment analysis, time series prediction, and multimodal learning.