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Model calibration usually requires optimizing some parameters (e.g.,temperature) w.r.t an objective function (e.g., negative log-likelihood). Inthis paper, we report a plain, important but often neglected fact that theobjective function is influenced by calibration set difficulty, i.e., the ratioof the number of incorrectly classified samples to that of correctly classifiedsamples. If a test set has a drastically different difficulty level from thecalibration set, the optimal calibration parameters of the two datasets wouldbe different. In other words, a calibrator optimal on the calibration set wouldbe suboptimal on the OOD test set and thus has degraded performance. With thisknowledge, we propose a simple and effective method named adaptive calibratorensemble (ACE) to calibrate OOD datasets whose difficulty is usually higherthan the calibration set. Specifically, two calibration functions are trained,one for in-distribution data (low difficulty), and the other for severely OODdata (high difficulty). To achieve desirable calibration on a new OOD dataset,ACE uses an adaptive weighting method that strikes a balance between the twoextreme functions. When plugged in, ACE generally improves the performance of afew state-of-the-art calibration schemes on a series of OOD benchmarks.Importantly, such improvement does not come at the cost of the in-distributioncalibration accuracy.

Adaptive Calibrator Ensemble for Model Calibration under Distribution Shift