  In order to safely deploy Deep Neural Networks (DNNs) within the perception
pipelines of real-time decision making systems, there is a need for safeguards
that can detect out-of-training-distribution (OoD) inputs both efficiently and
accurately. Building on recent work leveraging the local curvature of DNNs to
reason about epistemic uncertainty, we propose Sketching Curvature of OoD
Detection (SCOD), an architecture-agnostic framework for equipping any trained
DNN with a task-relevant epistemic uncertainty estimate. Offline, given a
trained model and its training data, SCOD employs tools from matrix sketching
to tractably compute a low-rank approximation of the Fisher information matrix,
which characterizes which directions in the weight space are most influential
on the predictions over the training data. Online, we estimate uncertainty by
measuring how much perturbations orthogonal to these directions can alter
predictions at a new test input. We apply SCOD to pre-trained networks of
varying architectures on several tasks, ranging from regression to
classification. We demonstrate that SCOD achieves comparable or better OoD
detection performance with lower computational burden relative to existing
baselines.
