  A set of novel approaches for estimating epistemic uncertainty in deep neural
networks with a single forward pass has recently emerged as a valid alternative
to Bayesian Neural Networks. On the premise of informative representations,
these deterministic uncertainty methods (DUMs) achieve strong performance on
detecting out-of-distribution (OOD) data while adding negligible computational
costs at inference time. However, it remains unclear whether DUMs are well
calibrated and can seamlessly scale to real-world applications - both
prerequisites for their practical deployment. To this end, we first provide a
taxonomy of DUMs, and evaluate their calibration under continuous
distributional shifts. Then, we extend them to semantic segmentation. We find
that, while DUMs scale to realistic vision tasks and perform well on OOD
detection, the practicality of current methods is undermined by poor
calibration under distributional shifts.
