Quantile Adaptive Temperature Scaling for Confidence Calibration
Quantile-Adaptive Temperature Scaling (QaTS) is a post-hoc calibration method that corrects poorly calibrated deep neural networks by adapting the prediction temperature based on a sample's confidence quantile. It uses an empirical quantile function to define a monotone temperature function, targeting miscalibration heterogeneity across the confidence spectrum, leading to superior Expected Calibration Error reduction.