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Generalized Out-of-Distribution Detection: A Survey

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Abstract

Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. The term, OOD detection, first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD), are closely related to OOD detection in terms of motivation and methodology. Despite common goals, these topics develop in isolation, and their subtle differences in definition and problem setting often confuse readers and practitioners. In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e.,AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. Despite comprehensive surveys of related fields, the summarization of OOD detection methods remains incomplete and requires further advancement. This paper specifically addresses the gap in recent technical developments in the field of OOD detection. It also provides a comprehensive discussion of representative methods from other sub-tasks and how they relate to and inspire the development of OOD detection methods. The survey concludes by identifying open challenges and potential research directions.

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Data Availability

The datasets analyzed during the current study in Sect. 5 are available in the OpenOOD repository, https://github.com/Jingkang50/OpenOOD.

Notes

  1. Align with MSP (Hendrycks & Gimpel, 2017) Check https://github.com/Jingkang50/OpenOOD/issues/206this issue in OpenOOD.

  2. OpenOOD provides a https://zjysteven.github.io/OpenOOD/leaderboard to track SOTAs.

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Acknowledgements

This study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 (MOE-T2EP20221-0012), NTU NAP, and under the RIE2020 Industry Alignment Fund—Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). YL is supported by the Office of the Vice Chancellor for Research and Graduate Education (OVCRGE) with funding from the Wisconsin Alumni Research Foundation (WARF).

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  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Jingkang Yang, Kaiyang Zhou & Ziwei Liu

  2. Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA

    Yixuan Li

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Yang, J., Zhou, K., Li, Y. et al. Generalized Out-of-Distribution Detection: A Survey. Int J Comput Vis 132, 5635–5662 (2024). https://doi.org/10.1007/s11263-024-02117-4

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