{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T08:08:09Z","timestamp":1785658089836,"version":"3.56.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T00:00:00Z","timestamp":1665878400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T00:00:00Z","timestamp":1665878400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,16]]},"DOI":"10.1109\/icip46576.2022.9897283","type":"proceedings-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T17:27:24Z","timestamp":1667496444000},"page":"1706-1710","source":"Crossref","is-referenced-by-count":133,"title":["Anomalib: A Deep Learning Library for Anomaly Detection"],"prefix":"10.1109","author":[{"given":"Samet","family":"Akcay","sequence":"first","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dick","family":"Ameln","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashwin","family":"Vaidya","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barath","family":"Lakshmanan","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilesh","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Utku","family":"Genc","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"MMDetection: Open MMLab Detection Toolbox and Benchmark","author":"Chen","year":"2019"},{"key":"ref2","article-title":"Detectron2","author":"Wu","year":"2019"},{"issue":"96","key":"ref3","first-page":"1","article-title":"PyOD: A Python Toolbox for Scalable Outlier Detection","volume":"20","author":"Zhao","year":"2019","journal-title":"Journal of Machine Learning Research"},{"key":"ref4","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume-title":"NeurIPS","author":"Paszke"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00104"},{"key":"ref6","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Tech. Rep"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ISIE45552.2021.9576231"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01476-x"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.45"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/info11020125"},{"key":"ref12","article-title":"Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection","author":"Ahuja","year":"2019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref17","article-title":"Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection","volume-title":"BMVC","author":"Wang"},{"issue":"85","key":"ref18","first-page":"2825","article-title":"Scikitlearn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"JMLR"},{"key":"ref19","article-title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","volume-title":"ICLR","author":"Sener"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-80129-8_17"},{"key":"ref21","article-title":"PyTorch Lightning","author":"Falcon","year":"2020"},{"key":"ref22","article-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","author":"Abadi","year":"2015","journal-title":"Tech. Rep"},{"key":"ref23","article-title":"Experiment Tracking with Weights and Biases","author":"Biewald","year":"2020"}],"event":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","location":"Bordeaux, France","start":{"date-parts":[[2022,10,16]]},"end":{"date-parts":[[2022,10,19]]}},"container-title":["2022 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9897158\/9897159\/09897283.pdf?arnumber=9897283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T16:17:59Z","timestamp":1705940279000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9897283\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,16]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icip46576.2022.9897283","relation":{},"subject":[],"published":{"date-parts":[[2022,10,16]]}}}