Training Label

Training labels, the annotations used to train machine learning models, are a critical component impacting model accuracy and efficiency. Current research focuses on improving label quality through techniques like self-supervised learning, label imputation, and addressing label noise (e.g., using adversarial machine learning or meta-learning approaches), particularly in scenarios with limited or noisy data. These advancements are crucial for various applications, including medical image segmentation, agricultural field boundary mapping, and hate speech detection, where accurate labels are essential for reliable model performance and practical deployment. The development of robust methods for handling imperfect labels is a significant area of ongoing investigation, aiming to improve the reliability and generalizability of machine learning models.