Calibration Map

Calibration maps are functions that adjust model predictions to improve the agreement between predicted confidence and actual accuracy, addressing the common problem of overconfidence in machine learning models. Current research focuses on developing novel calibration map algorithms, including those based on temperature scaling, focal loss, and geometric adjustments of neural network layers, as well as exploring methods for cautious calibration that prioritize underconfidence in high-risk scenarios. These advancements are crucial for enhancing the reliability and trustworthiness of machine learning systems across diverse applications, from computer vision (e.g., camera calibration) to tabular data analysis and semantic 3D mapping, improving decision-making in safety-critical contexts.