  Out-of-distribution detection is a common issue in deploying vision models in
practice and solving it is an essential building block in safety critical
applications. Most of the existing OOD detection solutions focus on improving
the OOD robustness of a classification model trained exclusively on
in-distribution (ID) data. In this work, we take a different approach and
propose to leverage generic pre-trained representation. We propose a novel OOD
method, called GROOD, that formulates the OOD detection as a Neyman-Pearson
task with well calibrated scores and which achieves excellent performance,
predicated by the use of a good generic representation. Only a trivial training
process is required for adapting GROOD to a particular problem. The method is
simple, general, efficient, calibrated and with only a few hyper-parameters.
The method achieves state-of-the-art performance on a number of OOD benchmarks,
reaching near perfect performance on several of them. The source code is
available at https://github.com/vojirt/GROOD.
