2020
Learn-By-Calibrating: Using Calibration As A Training Objective
ICASSP 2020accepted
Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial for training predictive models, even when we do not explicitly measure the uncertainties. This is conceptually similar t…