Label Purifier

Label purifiers are algorithms designed to improve the accuracy of machine learning models trained on datasets containing noisy or incorrect labels. Current research focuses on developing sophisticated architectures, such as those employing dual subnets or decoupled meta-learning, to effectively identify and correct these erroneous labels. These methods aim to enhance model generalization and robustness by leveraging techniques like local intrinsic dimensionality analysis or multi-stage label correction processes. The successful development of robust label purifiers has significant implications for various applications, improving the reliability and performance of machine learning models across diverse domains.