  Uncertainty estimation in deep learning has recently emerged as a crucial
area of interest to advance reliability and robustness in safety-critical
applications. While there have been many proposed methods that either focus on
distance-aware model uncertainties for out-of-distribution detection or on
input-dependent label uncertainties for in-distribution calibration, both of
these types of uncertainty are often necessary. In this work, we propose the
HetSNGP method for jointly modeling the model and data uncertainty. We show
that our proposed model affords a favorable combination between these two types
of uncertainty and thus outperforms the baseline methods on some challenging
out-of-distribution datasets, including CIFAR-100C, ImageNet-C, and ImageNet-A.
Moreover, we propose HetSNGP Ensemble, an ensembled version of our method which
additionally models uncertainty over the network parameters and outperforms
other ensemble baselines.
