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{https://github.com/edadaltocg/detectors}.

本文介绍了一种将离群分布检测评分无缝组合的通用方法，通过量化正态化将这些评分转化为 p 值，将问题转化为多变量假设检验，并使用元分析工具组合这些检验，从而得到具有巩固决策边界的更有效的检测器。通过实证研究，我们探索了不同类型的数据偏移对于数据的不同影响程度，结果表明我们的方法在各种离群分布检测场景中显著提高了整体鲁棒性和性能。值得注意的是，我们的框架在未来的检测评分开发中易于扩展，并且是这个领域中首个结合决策边界的方法。