{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T05:07:55Z","timestamp":1733634475387,"version":"3.30.1"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276030","62236003"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002766","name":"Beijing University of Posts and Telecommunications","doi-asserted-by":"publisher","award":["CX2023111"],"id":[{"id":"10.13039\/501100002766","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004602","name":"Program for New Century Excellent Talents in University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.patcog.2024.111212","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T23:09:13Z","timestamp":1732662553000},"page":"111212","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Unsupervised evaluation for out-of-distribution detection"],"prefix":"10.1016","volume":"160","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-5020","authenticated-orcid":false,"given":"Yuhang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jiani","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7311-1842","authenticated-orcid":false,"given":"Dongchao","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Weihong","family":"Deng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2024.111212_b1","doi-asserted-by":"crossref","DOI":"10.1007\/s11263-024-02117-4","article-title":"Generalized out-of-distribution detection: a survey","author":"Yang","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2024.111212_b2","article-title":"Semantic-driven dual consistency learning for weakly supervised video anomaly detection","author":"Su","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b3","article-title":"Discovering causally invariant features for out-of-distribution generalization","author":"Wang","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108931","article-title":"The familiarity hypothesis: Explaining the behavior of deep open set methods","author":"Dietterich","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b5","unstructured":"D. Hendrycks, K. Gimpel, A baseline for detecting misclassified and out-of-distribution examples in neural networks, in: ICLR, 2017."},{"key":"10.1016\/j.patcog.2024.111212_b6","unstructured":"S. Liang, Y. Li, R. Srikant, Enhancing the reliability of out-of-distribution image detection in neural networks, in: ICLR, 2018."},{"key":"10.1016\/j.patcog.2024.111212_b7","unstructured":"W. Liu, X. Wang, J. Owens, Y. Li, Energy-based out-of-distribution detection, in: NeurIPS, 2020."},{"key":"10.1016\/j.patcog.2024.111212_b8","doi-asserted-by":"crossref","unstructured":"X. Dong, J. Guo, A. Li, W.-T. Ting, C. Liu, H. Kung, Neural mean discrepancy for efficient out-of-distribution detection, in: CVPR, 2022.","DOI":"10.1109\/CVPR52688.2022.01862"},{"key":"10.1016\/j.patcog.2024.111212_b9","doi-asserted-by":"crossref","unstructured":"Y. Sun, Y. Li, Dice: Leveraging sparsification for out-of-distribution detection, in: ECCV, 2022.","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"10.1016\/j.patcog.2024.111212_b10","doi-asserted-by":"crossref","unstructured":"Q. Yu, K. Aizawa, Unsupervised out-of-distribution detection by maximum classifier discrepancy, in: ICCV, 2019.","DOI":"10.1109\/ICCV.2019.00961"},{"key":"10.1016\/j.patcog.2024.111212_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2021.02.007","article-title":"Outlier exposure with confidence control for out-of-distribution detection","author":"Papadopoulos","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2024.111212_b12","unstructured":"Y. Ming, Y. Fan, Y. Li, Poem: Out-of-distribution detection with posterior sampling, in: ICML, 2022."},{"key":"10.1016\/j.patcog.2024.111212_b13","doi-asserted-by":"crossref","unstructured":"Y. Li, N. Vasconcelos, Background data resampling for outlier-aware classification, in: CVPR, 2020.","DOI":"10.1109\/CVPR42600.2020.01323"},{"key":"10.1016\/j.patcog.2024.111212_b14","doi-asserted-by":"crossref","unstructured":"X. Wu, J. Lu, Z. Fang, G. Zhang, Meta ood learning for continuously adaptive ood detection, in: ICCV, 2023.","DOI":"10.1109\/ICCV51070.2023.01773"},{"key":"10.1016\/j.patcog.2024.111212_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108897","article-title":"Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection","author":"Zhu","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108703","article-title":"Unsupervised video anomaly detection via normalizing flows with implicit latent features","author":"Cho","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b17","unstructured":"W. Deng, S. Gould, L. Zheng, What does rotation prediction tell us about classifier accuracy under varying testing environments?, in: ICML, 2021."},{"key":"10.1016\/j.patcog.2024.111212_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124922","article-title":"Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods","author":"Mejri","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2024.111212_b19","article-title":"A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos","author":"Zhong","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2006.12.009","article-title":"Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection","author":"Tsang","year":"2007","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2024.111212_b21","doi-asserted-by":"crossref","unstructured":"W. Deng, L. Zheng, Are labels always necessary for classifier accuracy evaluation?, in: CVPR, 2021.","DOI":"10.1109\/CVPR46437.2021.01482"},{"key":"10.1016\/j.patcog.2024.111212_b22","unstructured":"S. Garg, S. Balakrishnan, Z.C. Lipton, B. Neyshabur, H. Sedghi, Leveraging unlabeled data to predict out-of-distribution performance, in: ICLR, 2022."},{"key":"10.1016\/j.patcog.2024.111212_b23","doi-asserted-by":"crossref","unstructured":"Z. Li, K. Kamnitsas, M. Islam, C. Chen, B. Glocker, Estimating model performance under domain shifts with class-specific confidence scores, in: MICCAI, 2022.","DOI":"10.1007\/978-3-031-16449-1_66"},{"key":"10.1016\/j.patcog.2024.111212_b24","doi-asserted-by":"crossref","unstructured":"D. Guillory, V. Shankar, S. Ebrahimi, T. Darrell, L. Schmidt, Predicting with confidence on unseen distributions, in: ICCV, 2021.","DOI":"10.1109\/ICCV48922.2021.00117"},{"key":"10.1016\/j.patcog.2024.111212_b25","unstructured":"D. Ji, P. Smyth, M. Steyvers, Can I trust my fairness metric? assessing fairness with unlabeled data and bayesian inference, in: NeurIPS, 2020."},{"key":"10.1016\/j.patcog.2024.111212_b26","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Tech Report"},{"key":"10.1016\/j.patcog.2024.111212_b27","doi-asserted-by":"crossref","unstructured":"F. Yu, D. Wang, E. Shelhamer, T. Darrell, Deep layer aggregation, in: CVPR, 2018.","DOI":"10.1109\/CVPR.2018.00255"},{"year":"2019","series-title":"Scaling out-of-distribution detection for real-world settings","author":"Hendrycks","key":"10.1016\/j.patcog.2024.111212_b28"},{"key":"10.1016\/j.patcog.2024.111212_b29","unstructured":"T. Xiao, T. Xia, Y. Yang, C. Huang, X. Wang, Learning from massive noisy labeled data for image classification, in: CVPR, 2015."},{"key":"10.1016\/j.patcog.2024.111212_b30","unstructured":"J. Chen, F. Liu, B. Avci, X. Wu, Y. Liang, S. Jha, Detecting errors and estimating accuracy on unlabeled data with self-training ensembles, in: NeurIPS, 2021."},{"year":"2014","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","key":"10.1016\/j.patcog.2024.111212_b31"},{"key":"10.1016\/j.patcog.2024.111212_b32","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: CVPR, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.patcog.2024.111212_b33","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: CVPR, 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.patcog.2024.111212_b34","unstructured":"S. Vaze, K. Han, A. Vedaldi, A. Zisserman, Open-set recognition: A good closed-set classifier is all you need, in: ICLR, 2022."},{"key":"10.1016\/j.patcog.2024.111212_b35","unstructured":"X. Du, Z. Fang, I. Diakonikolas, Y. Li, How does unlabeled data provably help out-of-distribution detection?, in: ICLR, 2024."},{"key":"10.1016\/j.patcog.2024.111212_b36","doi-asserted-by":"crossref","DOI":"10.1109\/TBIOM.2023.3242085","article-title":"Masked face recognition dataset and application","author":"Wang","year":"2023","journal-title":"IEEE Trans. Biometrics Behav. Identity Sci."},{"key":"10.1016\/j.patcog.2024.111212_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320324009634?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320324009634?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:01:02Z","timestamp":1733562062000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320324009634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":37,"alternative-id":["S0031320324009634"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2024.111212","relation":{},"ISSN":["0031-3203"],"issn-type":[{"type":"print","value":"0031-3203"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unsupervised evaluation for out-of-distribution detection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2024.111212","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111212"}}