Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning (2020-02-15T00:00:00.000000Z)

TL;DR

This work focuses on in-domain uncertainty for image classification and introduces the deep ensemble equivalent (DEE) and shows that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.

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 the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.

Authors

Arsenii Ashukha

3 papers

Alexander Lyzhov

1 papers

Dmitry Molchanov

2 papers

TL;DR

Abstract

Authors

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