Class Label

Class labels, the categorical identifiers assigned to data points, are fundamental to supervised machine learning but present ongoing challenges. Research focuses on improving the efficiency and accuracy of label generation and utilization, including developing methods for handling noisy or incomplete labels, leveraging unsupervised techniques to create pseudo-labels, and exploring the relationship between labels and underlying data representations. These advancements are crucial for improving the performance and robustness of machine learning models across diverse applications, from image recognition and natural language processing to medical diagnosis and robotics.