  Being uncertain when facing the unknown is key to intelligent decision
making. However, machine learning algorithms lack reliable estimates about
their predictive uncertainty. This leads to wrong and overly-confident
decisions when encountering classes unseen during training. Despite the
importance of equipping classifiers with uncertainty estimates ready for the
real world, prior work has focused on small datasets and little or no class
discrepancy between training and testing data. To close this gap, we introduce
UIMNET: a realistic, ImageNet-scale test-bed to evaluate predictive uncertainty
estimates for deep image classifiers. Our benchmark provides implementations of
eight state-of-the-art algorithms, six uncertainty measures, four in-domain
metrics, three out-domain metrics, and a fully automated pipeline to train,
calibrate, ensemble, select, and evaluate models. Our test-bed is open-source
and all of our results are reproducible from a fixed commit in our repository.
Adding new datasets, algorithms, measures, or metrics is a matter of a few
lines of code-in so hoping that UIMNET becomes a stepping stone towards
realistic, rigorous, and reproducible research in uncertainty estimation. Our
results show that ensembles of ERM classifiers as well as single MIMO
classifiers are the two best alternatives currently available to measure
uncertainty about both in-domain and out-domain classes.
