Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/179441
Title: Generalized out-of-distribution detection: a survey
Authors: Yang, Jingkang
Zhou, Kaiyang
Li, Yixuan
Liu, Ziwei
Keywords: Computer and Information Science
Issue Date: 2024
Source: Yang, J., Zhou, K., Li, Y. & Liu, Z. (2024). Generalized out-of-distribution detection: a survey. International Journal of Computer Vision. https://dx.doi.org/10.1007/s11263-024-02117-4
Project: MOE-T2EP20221-0012 
NTU NAP 
IAF-ICP 
Journal: International Journal of Computer Vision
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.
URI: https://hdl.handle.net/10356/179441
ISSN: 0920-5691
DOI: 10.1007/s11263-024-02117-4
Schools: School of Computer Science and Engineering 
Research Centres: S-Lab
Rights: © 2024 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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