# Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning Source: [Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:48.909Z Authors: Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov Published: 2020-02-15T23:28:19.000Z ## Abstract Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in this study, we introduce the deep ensemble equivalent score (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of only few independently trained networks in terms of test performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()