Calibration Network

Calibration networks aim to improve the reliability of deep learning models by ensuring their predicted confidence scores accurately reflect the true probability of their predictions. Current research focuses on addressing issues like overconfidence, particularly in challenging scenarios such as imbalanced datasets and out-of-distribution data, employing techniques such as temperature scaling, focal loss modifications, and ensemble methods. These advancements are crucial for deploying reliable AI systems in high-stakes applications like medical image analysis and autonomous robotics, where accurate confidence estimation is paramount for safe and effective operation.