  Since the seminal paper of Hendrycks et al. arXiv:1610.02136, Post-hoc deep
Out-of-Distribution (OOD) detection has expanded rapidly. As a result,
practitioners working on safety-critical applications and seeking to improve
the robustness of a neural network now have a plethora of methods to choose
from. However, no method outperforms every other on every dataset
arXiv:2210.07242, so the current best practice is to test all the methods on
the datasets at hand. This paper shifts focus from developing new methods to
effectively combining existing ones to enhance OOD detection. We propose and
compare four different strategies for integrating multiple detection scores
into a unified OOD detector, based on techniques such as majority vote,
empirical and copulas-based Cumulative Distribution Function modeling, and
multivariate quantiles based on optimal transport. We extend common OOD
evaluation metrics -- like AUROC and FPR at fixed TPR rates -- to these
multi-dimensional OOD detectors, allowing us to evaluate them and compare them
with individual methods on extensive benchmarks. Furthermore, we propose a
series of guidelines to choose what OOD detectors to combine in more realistic
settings, i.e. in the absence of known OOD data, relying on principles drawn
from Outlier Exposure arXiv:1812.04606. The code is available at
https://github.com/paulnovello/multi-ood.
