  We address the problem of uncertainty calibration and introduce a novel
calibration method, Parametrized Temperature Scaling (PTS). Standard deep
neural networks typically yield uncalibrated predictions, which can be
transformed into calibrated confidence scores using post-hoc calibration
methods. In this contribution, we demonstrate that the performance of
accuracy-preserving state-of-the-art post-hoc calibrators is limited by their
intrinsic expressive power. We generalize temperature scaling by computing
prediction-specific temperatures, parameterized by a neural network. We show
with extensive experiments that our novel accuracy-preserving approach
consistently outperforms existing algorithms across a large number of model
architectures, datasets and metrics.
