Robust Calibration

Robust calibration aims to improve the accuracy and reliability of models and systems by reducing discrepancies between predicted confidence and actual performance, particularly when dealing with out-of-distribution data or noisy measurements. Current research focuses on developing methods to mitigate miscalibration in various contexts, including deep neural networks, computer vision systems (e.g., camera and LiDAR calibration), and traffic simulations, often employing techniques like temperature scaling, dynamic regularization, and advanced optimization algorithms such as bundle adjustment. These advancements are crucial for enhancing the trustworthiness and applicability of machine learning models and sensor systems across diverse fields, from autonomous driving to medical imaging.