  This paper introduces a universal approach to seamlessly combine
out-of-distribution (OOD) detection scores. These scores encompass a wide range
of techniques that leverage the self-confidence of deep learning models and the
anomalous behavior of features in the latent space. Not surprisingly, combining
such a varied population using simple statistics proves inadequate. To overcome
this challenge, we propose a quantile normalization to map these scores into
p-values, effectively framing the problem into a multi-variate hypothesis test.
Then, we combine these tests using established meta-analysis tools, resulting
in a more effective detector with consolidated decision boundaries.
Furthermore, we create a probabilistic interpretable criterion by mapping the
final statistics into a distribution with known parameters. Through empirical
investigation, we explore different types of shifts, each exerting varying
degrees of impact on data. Our results demonstrate that our approach
significantly improves overall robustness and performance across diverse OOD
detection scenarios. Notably, our framework is easily extensible for future
developments in detection scores and stands as the first to combine decision
boundaries in this context. The code and artifacts associated with this work
are publicly available\footnote{\url{https://github.com/edadaltocg/detectors}}.
