{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:22:41Z","timestamp":1740100961145,"version":"3.37.3"},"reference-count":48,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["16KIS0933K"],"award-info":[{"award-number":["16KIS0933K"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020639","name":"Bavarian Ministry of Economic Affairs, Regional Development and Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020639","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892807","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Anomaly Detection by Recombining Gated Unsupervised Experts"],"prefix":"10.1109","author":[{"given":"Jan-Philipp","family":"Schulze","sequence":"first","affiliation":[{"name":"Technical University of Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Sperl","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Bottinger","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Applied and Integrated Security,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2017.8104917"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref33","first-page":"582","article-title":"Support vector method for novelty detection","author":"sch\u00f6lkopf","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref31","article-title":"A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges","author":"salehi","year":"2021","journal-title":"arXiv 2110 14051 [cs]"},{"key":"ref30","article-title":"Deep Semi-Supervised Anomaly Detection","author":"ruff","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67661-2_5"},{"key":"ref36","article-title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","author":"shazeer","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"journal-title":"SSD A Unified Framework for Self-Supervised Outlier Detection","year":"2020","author":"sehwag","key":"ref34"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3442520.3442521"},{"key":"ref40","first-page":"550","author":"vyas","year":"2018","journal-title":"Out-of-distribution detection using an ensemble of self supervised leave-out classifiers"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16530"},{"key":"ref12","first-page":"770","author":"he","year":"2016","journal-title":"Deep residual learning for image recognition"},{"key":"ref13","article-title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","author":"hendrycks","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"journal-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","year":"2018","author":"liang","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref19","article-title":"Adversarial Autoencoders","author":"makhzani","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019428"},{"key":"ref27","first-page":"8764","article-title":"A General Framework For Detecting Anomalous Inputs to DNN Classifiers","author":"raghuram","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0168-1699(99)00046-0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102652"},{"key":"ref29","first-page":"4393","article-title":"Deep One-Class Classification","author":"ruff","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(02)00577-5"},{"journal-title":"UCI Machine Learning Repository","year":"2017","author":"dua","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5724"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1498"},{"key":"ref1","first-page":"622","article-title":"GANomaly: Semi-supervised Anomaly Detection via Adversarial Training","author":"akcay","year":"2019","journal-title":"Computer Vision - ACCV 2018"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01944-5"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46298-1_30"},{"key":"ref45","first-page":"3314","volume":"3","author":"ye","year":"2021","journal-title":"Understanding the Effect of Bias in Deep Anomaly Detection"},{"key":"ref48","article-title":"Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection","author":"zong","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2200299"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(98)00120-9"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281279"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220042"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"ref23","first-page":"756","article-title":"D&#x00CF;oT: A Federated Self-learning Anomaly Detection System for IoT","author":"nguyen","year":"2019","journal-title":"2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)"},{"key":"ref44","article-title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","author":"xiao","year":"2017","journal-title":"arXiv 1708 07747 [cs stat]"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330871"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC47002.2019.00034"},{"key":"ref25","first-page":"38:1","article-title":"Deep Learning for Anomaly Detection: A Review","volume":"54","author":"pang","year":"2021","journal-title":"ACM Computing Surveys"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2022,7,18]]},"location":"Padua, Italy","end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892807.pdf?arnumber=9892807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:58:14Z","timestamp":1667516294000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892807\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892807","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}