Evaluating DNN uncertainty: four methods and the error–accuracy trade-off

Overview

Adrian Schwaiger compares Monte Carlo dropout, deep ensembles, learned confidence and evidential deep learning for image classification. After uncertain predictions are filtered out, RER and RAR express the remaining error and correct-prediction counts relative to all predictions. The article stresses that the trade-off depends on the dataset, architecture and distribution shift.