Uncertainty for deep image classifiers on out of distribution data.
In addition to achieving high accuracy, in many applications, it is important to estimate the probability that a model prediction is correct. Predictive uncertainty is particularly important on out of distribution (OOD) data where accuracy degrades. However, models are typically overconfident, and model calibration on OOD data remains a challenge. In this paper we propose a simple post hoc calibration method that significantly improves on benchmark results [Ovadia et al 2019] on a wide range of corrupted data. Our method uses outlier exposure to properly calibrate the model probabilities.
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