Labeled Sample

Labeled samples are crucial for training machine learning models, but datasets often contain mislabeled data, hindering model accuracy and robustness. Current research focuses on developing methods to identify and correct these errors, employing techniques like teacher-student learning, collaborative learning between multiple models, and analysis of training dynamics to detect inconsistencies. These advancements are significant because accurate labeling is essential for reliable model performance across various applications, from image recognition to graph analysis and electrochemical impedance spectroscopy. Improved methods for handling mislabeled samples directly translate to more accurate and trustworthy machine learning models.