{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:52:45Z","timestamp":1785603165325,"version":"3.56.0"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Research Foundation, Prime Minister’s Office, Singapore"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tai.2025.3550473","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T13:51:18Z","timestamp":1741787478000},"page":"1213-1222","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Hierarchical Graph Cut for Multi-Granularity Out-of-Distribution Detection"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-5771","authenticated-orcid":false,"given":"Xiang","family":"Fang","sequence":"first","affiliation":[{"name":"Energy Research Institute, Interdisciplinary Graduate Programme and College of Computing and Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-3847","authenticated-orcid":false,"given":"Arvind","family":"Easwaran","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5758-1876","authenticated-orcid":false,"given":"Blaise","family":"Genest","sequence":"additional","affiliation":[{"name":"CNRS IPAL IRL, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0901-5105","authenticated-orcid":false,"given":"Ponnuthurai Nagaratnam","family":"Suganthan","sequence":"additional","affiliation":[{"name":"KINDI Computing Research Center, College of Engineering, Qatar University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3108126"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106626"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3135248"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00320"},{"key":"ref5","first-page":"1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks","year":"2016"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00819"},{"key":"ref7","first-page":"1","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liang","year":"2018"},{"key":"ref8","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref9","first-page":"144","article-title":"REACT: Out-of-distribution detection with rectified activations","volume":"34","author":"Sun","year":"2021","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref10","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume":"31","author":"Lee","year":"2018","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00961"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00933"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref16","first-page":"1","article-title":"How to exploit hyperspherical embeddings for out-of-distribution detection?\u201d","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Ming","year":"2023"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3106743"},{"key":"ref18","first-page":"1","article-title":"VOS: Learning what you don\u2019t know by virtual outlier synthesis","author":"Du","year":"2021","journal-title":"Proc. ICLR"},{"key":"ref19","first-page":"1","article-title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples","volume-title":"Proc. 6th Int. Conf. Learn. Representations (ICLR)","author":"Lee","year":"2018"},{"key":"ref20","article-title":"Building robust classifiers through generation of confident out of distribution examples","author":"Sricharan","year":"2018"},{"key":"ref21","article-title":"Out-of-distribution detection in classifiers via generation","author":"Vernekar","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20670"},{"key":"ref23","first-page":"1179","article-title":"Single layer predictive normalized maximum likelihood for out-of-distribution detection","volume-title":"Proc. NeurIPS","author":"Bibas","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00114"},{"key":"ref25","first-page":"20578","article-title":"Why normalizing flows fail to detect out-of-distribution data","volume-title":"Proc. NeurIPS","author":"Kirichenko","year":"2020"},{"key":"ref26","first-page":"1","article-title":"Input complexity and out-of-distribution detection with likelihood-based generative models","author":"Serr\u00e0","year":"2019","journal-title":"Proc. ICLR"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69538-5_4"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00723"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_22"},{"key":"ref30","first-page":"15650","article-title":"Poem: Out-of-distribution detection with posterior sampling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ming","year":"2022"},{"key":"ref31","first-page":"1","article-title":"Conjnorm: Tractable density estimation for out-of-distribution detection","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Peng","year":"2024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3316628"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-28831-7_5"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3374580"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref36","first-page":"1","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018","journal-title":"Proc. ICLR"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2485782"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.05.024"},{"key":"ref39","volume-title":"Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms","author":"Rosenblatt","year":"1962"},{"key":"ref40","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref42","first-page":"1","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. NIPS Workshop","author":"Yuval","year":"2011"},{"issue":"7","key":"ref43","first-page":"3","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref44","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"Yu","year":"2015"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref46","first-page":"37199","article-title":"Is out-of-distribution detection learnable?\u201d","volume":"35","author":"Fang","year":"2022","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"issue":"84","key":"ref47","first-page":"1","article-title":"On the learnability of out-of-distribution detection","volume":"25","author":"Fang","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref49","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Proc. Adv. Neural Inf. Process. Syst."}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/11417361\/10923752.pdf?arnumber=10923752","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T20:59:00Z","timestamp":1772485140000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10923752\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":49,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tai.2025.3550473","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}