A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
This addresses the critical need for reliable uncertainty estimation in real-world applications where training and test data distributions differ, offering a domain-specific incremental improvement.
The paper tackles the problem of calibrating deep neural networks under domain shift by proposing Unsupervised Temperature Scaling (UTS), which uses unlabeled test samples to adjust uncertainty predictions, demonstrating robustness compared to existing methods in shifted domains.
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent. Many calibration methods in the literature have been proposed to improve the predictive uncertainty of DNNs which are generally not well-calibrated. However, none of them is specifically designed to work properly under domain shift condition. In this paper, we propose Unsupervised Temperature Scaling (UTS) as a robust calibration method to domain shift. It exploits unlabeled test samples instead of the training one to adjust the uncertainty prediction of deep models towards the test distribution. UTS utilizes a novel loss function, weighted NLL, which allows unsupervised calibration. We evaluate UTS on a wide range of model-datasets to show the possibility of calibration without labels and demonstrate the robustness of UTS compared to other methods (e.g., TS, MC-dropout, SVI, ensembles) in shifted domains.