da18c47118a2d09926346f33bebde9f4-Paper-Conference.pdf

If diversity does in fact explain UQ/robustness improvements, this would suggest that deep ensembles indeed offer benefits that cannot be obtained by (standard) single neural networks. In this paper, we rigorously test hypotheses that formalize this intuition. Surprisingly, after controlling for factors related to the performance of an ensemble's component models, we find no evidence that having a diverse set of predictions is responsible for these purported benefits.