Label Calibration

Label calibration addresses the problem of unreliable confidence scores produced by machine learning models, aiming to improve the accuracy and trustworthiness of predictions. Current research focuses on adapting calibration techniques to multi-label scenarios, handling class imbalances, and mitigating label bias in various applications, including image recognition, natural language processing, and medical image analysis. These advancements are crucial for enhancing the reliability and interpretability of machine learning models across diverse fields, leading to more robust and trustworthy decision-making systems. Methods explored include employing techniques like conformal prediction, pseudo-label refinement, and incorporating relationships between labels to improve calibration accuracy.