{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:27:01Z","timestamp":1775665621915,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["N00014-19-1-2728"],"award-info":[{"award-number":["N00014-19-1-2728"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2035038"],"award-info":[{"award-number":["2035038"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1109\/tpami.2023.3328883","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T17:56:21Z","timestamp":1698774981000},"page":"944-956","source":"Crossref","is-referenced-by-count":9,"title":["WOOD: Wasserstein-Based Out-of-Distribution Detection"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4079-1658","authenticated-orcid":false,"given":"Yinan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3811-5853","authenticated-orcid":false,"given":"Wenbo","family":"Sun","sequence":"additional","affiliation":[{"name":"Transportation Research Institute, University of Michigan, Ann Arbor, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7786-8110","authenticated-orcid":false,"given":"Jionghua","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8827-502X","authenticated-orcid":false,"given":"Zhenyu","family":"Kong","sequence":"additional","affiliation":[{"name":"Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6019-0940","authenticated-orcid":false,"given":"Xiaowei","family":"Yue","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Institute for Quality and Reliability, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/BF00532240"},{"key":"ref7","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref8","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref10","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Lee","year":"2018"},{"key":"ref11","article-title":"Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance","author":"Denouden","year":"2018"},{"key":"ref12","article-title":"Confidence from invariance to image transformations","author":"Bahat","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99978-4_9"},{"key":"ref14","first-page":"5546","article-title":"To trust or not to trust a classifier","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00933"},{"key":"ref16","article-title":"Deep anomaly detection with outlier exposure","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86523-8_26"},{"key":"ref18","article-title":"Learning confidence for out-of-distribution detection in neural networks","author":"DeVries","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"ref20","first-page":"21 464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref21","article-title":"From GAN to WGAN","author":"Weng","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2022.1275"},{"key":"ref23","first-page":"1308","article-title":"Deep active learning: Unified and principled method for query and training","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Shui"},{"key":"ref24","article-title":"Propagating uncertainty in reinforcement learning via Wasserstein barycenters","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Metelli","year":"2019"},{"key":"ref25","article-title":"Learning with a wasserstein loss","volume-title":"Advances in Neural Information Processing Systems","volume":"28","author":"Frogner","year":"2015"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2021.3118635"},{"key":"ref27","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref28","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref29","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume-title":"Advances in Neural Information Processing Systems","volume":"26","author":"Cuturi","year":"2013"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1162\/153244303321897690"},{"key":"ref32","article-title":"MNIST handwritten digit database","author":"LeCun","year":"2010"},{"key":"ref33","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref34","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459199"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20212-4"},{"issue":"4","key":"ref42","article-title":"New estimates of the kullback-leibler distance and applications","volume":"3","author":"Dragomir","year":"2000","journal-title":"RGMIA Res. Rep. Collection"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/10384454\/10302348-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10384454\/10302348.pdf?arnumber=10302348","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:50:37Z","timestamp":1705024237000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10302348\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":42,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3328883","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]}}}