{"ddc":["000"],"arxiv":1,"_id":"21070","citation":{"apa":"Demirel, B., Fumero, M., &#38; Locatello, F. (2025). Out-of-Distribution detection with relative angles. In <i>39th Annual Conference on Neural Information Processing Systems</i> (Vol. 38). San Diego, CA, United States: Neural Information Processing Systems Foundation.","ieee":"B. Demirel, M. Fumero, and F. Locatello, “Out-of-Distribution detection with relative angles,” in <i>39th Annual Conference on Neural Information Processing Systems</i>, San Diego, CA, United States, 2025, vol. 38.","ista":"Demirel B, Fumero M, Locatello F. 2025. Out-of-Distribution detection with relative angles. 39th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 38.","mla":"Demirel, Berker, et al. “Out-of-Distribution Detection with Relative Angles.” <i>39th Annual Conference on Neural Information Processing Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025.","ama":"Demirel B, Fumero M, Locatello F. Out-of-Distribution detection with relative angles. In: <i>39th Annual Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information Processing Systems Foundation; 2025.","short":"B. Demirel, M. Fumero, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.","chicago":"Demirel, Berker, Marco  Fumero, and Francesco Locatello. “Out-of-Distribution Detection with Relative Angles.” In <i>39th Annual Conference on Neural Information Processing Systems</i>, Vol. 38. Neural Information Processing Systems Foundation, 2025."},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2410.04525"}],"quality_controlled":"1","date_published":"2025-12-01T00:00:00Z","status":"public","acknowledgement":"This research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC BY public copyright license to any accepted manuscript version arising from this submission.\r\n","OA_place":"repository","OA_type":"green","abstract":[{"lang":"eng","text":"Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable model should ideally abstain from making decisions in this out-of-distribution (OOD) setting. Existing state-of-the-art methods primarily focus on feature distances, such as k-th nearest neighbors and distances to decision boundaries, either overlooking or ineffectively using in-distribution statistics. In this work, we propose a novel angle-based metric for OOD detection that is computed relative to the in-distribution structure. We demonstrate that the angles between feature representations and decision boundaries, viewed from the mean of in-distribution features, serve as an effective discriminative factor between ID and OOD data. We evaluate our method on nine ImageNet-pretrained models. Our approach achieves the lowest FPR in 5 out of 9 ImageNet models, obtains the best average FPR overall, and consistently ranking among the top 3 across all evaluated models. Furthermore, we highlight the benefits of contrastive representations by showing strong performance with ResNet SCL and CLIP architectures. Finally, we demonstrate that the scale-invariant nature of our score enables an ensemble strategy via simple score summation. "}],"language":[{"iso":"eng"}],"title":"Out-of-Distribution detection with relative angles","month":"12","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        38","day":"01","author":[{"full_name":"Demirel, Berker","last_name":"Demirel","first_name":"Berker","id":"8b4bc47f-3200-11ee-973b-8f0e7be21a9f"},{"full_name":"Fumero, Marco ","first_name":"Marco ","last_name":"Fumero"},{"orcid":"0000-0002-4850-0683","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco"}],"related_material":{"link":[{"relation":"software","url":"https://github.com/berkerdemirel/ORA-OOD-Detection-with-Relative-Angles"}]},"type":"conference","publication":"39th Annual Conference on Neural Information Processing Systems","article_processing_charge":"No","conference":{"name":"NeurIPS: Neural Information Processing Systems","end_date":"2025-12-07","start_date":"2025-12-02","location":"San Diego, CA, United States"},"date_created":"2026-01-29T14:26:47Z","date_updated":"2026-02-16T11:38:25Z","volume":38,"department":[{"_id":"FrLo"}],"publication_status":"epub_ahead","publication_identifier":{"issn":["1049-5258"]},"alternative_title":["Advances in Neural Information Processing Systems"],"external_id":{"arxiv":["2410.04525"]},"has_accepted_license":"1","year":"2025","publisher":"Neural Information Processing Systems Foundation","corr_author":"1","oa_version":"Preprint"}