  Ensembling neural networks is an effective way to increase accuracy, and can
often match the performance of individual larger models. This observation poses
a natural question: given the choice between a deep ensemble and a single
neural network with similar accuracy, is one preferable over the other? Recent
work suggests that deep ensembles may offer distinct benefits beyond predictive
power: namely, uncertainty quantification and robustness to dataset shift. In
this work, we demonstrate limitations to these purported benefits, and show
that a single (but larger) neural network can replicate these qualities. First,
we show that ensemble diversity, by any metric, does not meaningfully
contribute to an ensemble's uncertainty quantification on out-of-distribution
(OOD) data, but is instead highly correlated with the relative improvement of a
single larger model. Second, we show that the OOD performance afforded by
ensembles is strongly determined by their in-distribution (InD) performance,
and -- in this sense -- is not indicative of any "effective robustness". While
deep ensembles are a practical way to achieve improvements to predictive power,
uncertainty quantification, and robustness, our results show that these
improvements can be replicated by a (larger) single model.
