Unsupervised Calibration

Unsupervised calibration aims to improve the reliability and accuracy of machine learning models' predictions without relying on labeled data from the target domain. Current research focuses on adapting models to shifting data distributions, often leveraging unlabeled data to adjust model outputs or uncertainty estimates, employing techniques like temperature scaling and generative models. These methods address the critical need for robust and reliable model performance in real-world scenarios where labeled data is scarce or expensive, impacting various applications from object recognition to natural language processing. The development of source-free calibration methods, which require no labeled data from the original training domain, represents a significant advancement in this area.