  Recent work introduced the epinet as a new approach to uncertainty modeling
in deep learning. An epinet is a small neural network added to traditional
neural networks, which, together, can produce predictive distributions. In
particular, using an epinet can greatly improve the quality of joint
predictions across multiple inputs, a measure of how well a neural network
knows what it does not know. In this paper, we examine whether epinets can
offer similar advantages under distributional shifts. We find that, across
ImageNet-A/O/C, epinets generally improve robustness metrics. Moreover, these
improvements are more significant than those afforded by even very large
ensembles at orders of magnitude lower computational costs. However, these
improvements are relatively small compared to the outstanding issues in
distributionally-robust deep learning. Epinets may be a useful tool in the
toolbox, but they are far from the complete solution.
