Domain Calibration

Domain calibration in machine learning focuses on improving the reliability and trustworthiness of models when applied to data from different domains than those used for training. Current research emphasizes techniques like temperature scaling, loss regularization, and diffusion-based methods to enhance model calibration, often incorporating domain-specific knowledge or leveraging unsupervised learning approaches. These advancements are crucial for deploying reliable AI systems in diverse real-world applications, particularly in high-stakes domains like healthcare and autonomous driving, where accurate uncertainty quantification is paramount. The ultimate goal is to build models that not only achieve high accuracy but also provide well-calibrated confidence estimates across various data distributions.