  We propose a method for training a deterministic deep model that can find and
reject out of distribution data points at test time with a single forward pass.
Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas
of RBF networks. We scale training in these with a novel loss function and
centroid updating scheme and match the accuracy of softmax models. By enforcing
detectability of changes in the input using a gradient penalty, we are able to
reliably detect out of distribution data. Our uncertainty quantification scales
well to large datasets, and using a single model, we improve upon or match Deep
Ensembles in out of distribution detection on notable difficult dataset pairs
such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN.
