  One critical challenge in deploying highly performant machine learning models
in real-life applications is out of distribution (OOD) detection. Given a
predictive model which is accurate on in distribution (ID) data, an OOD
detection system will further equip the model with the option to defer
prediction when the input is novel and the model has little confidence in
prediction. There has been some recent interest in utilizing the gradient
information in pre-trained models for OOD detection. While these methods have
shown competitive performance, there are misconceptions about the true
mechanism underlying them, which conflate their performance with the necessity
of gradients. In this work, we provide an in-depth analysis and comparison of
gradient based methods and elucidate the key components that warrant their OOD
detection performance. We further propose a general, non-gradient based method
of OOD detection which improves over previous baselines in both performance and
computational efficiency.
