Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Many calibration methods of varying complexity have been proposed for the task, but there is no consensus on how expressive these should be. We focus on …