{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:41:04Z","timestamp":1740102064805,"version":"3.37.3"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1109\/cacre58689.2023.10208545","type":"proceedings-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T17:24:39Z","timestamp":1691515479000},"page":"144-149","source":"Crossref","is-referenced-by-count":0,"title":["Continual Contrastive Anomaly Detection under Natural Data Distribution Shifts"],"prefix":"10.1109","author":[{"given":"Jingyu","family":"Yang","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology,School of Artificial Intelligence and Automation,Wuhan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Shen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology,School of Artificial Intelligence and Automation,Wuhan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linan","family":"Deng","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology,School of Mechanical Science and Engineering,Wuhan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref12","first-page":"3987","article-title":"Continual learning through synaptic intelligence","author":"zenke","year":"2017","journal-title":"International Conference on Machine Learning"},{"year":"2016","author":"rusu","article-title":"Progressive neural networks","key":"ref15"},{"key":"ref14","article-title":"Variational continual learning","author":"nguyen","year":"2018","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1613\/jair.3623"},{"key":"ref30","article-title":"Anomaly detection for tabular data with internal contrastive learning","author":"shenkar","year":"2022","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1073\/pnas.1611835114"},{"key":"ref10","article-title":"Online coreset selection for rehearsal-based continual learning","author":"yoon","year":"2022","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1145\/1978672.1978676"},{"key":"ref2","article-title":"Deep semi-supervised anomaly detection","author":"ruff","year":"2020","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1109\/CVPRW50498.2020.00135"},{"key":"ref17","article-title":"Lifelong learning with dynamically expandable networks","author":"yoon","year":"2018","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR.2017.753"},{"key":"ref19","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"de lange","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref18","article-title":"A neural dirichlet process mixture model for task-free continual learning","author":"lee","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref24","article-title":"Graph-augmented normalizing flows for anomaly detection of multiple time series","author":"dai","year":"2022","journal-title":"International Conference on Learning Representations"},{"key":"ref23","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","author":"zong","year":"2018","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/CVPR.2019.00301"},{"key":"ref25","article-title":"Generative probabilistic novelty detection with adversarial autoencoders","volume":"31","author":"pidhorskyi","year":"2018","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1007\/978-3-031-19806-9_7"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/CVPRW56347.2022.00432"},{"year":"2021","author":"han","article-title":"Elsa: Energy-based learning for semi-supervised anomaly detection","key":"ref21"},{"key":"ref28","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"International Conference on Machine Learning"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1162\/089976601750264965"},{"key":"ref29","first-page":"11 839","article-title":"Csi: Novelty detection via contrastive learning on distributionally shifted instances","volume":"33","author":"tack","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Online continual learning with maximal interfered retrieval","volume":"32","author":"aljundi","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref7","article-title":"Experience replay for continual learning","volume":"32","author":"rolnick","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref9","article-title":"Gradient based sample selection for online continual learning","volume":"32","author":"aljundi","year":"2019","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/ICCV48922.2021.00817"},{"key":"ref3","first-page":"32 854","article-title":"Anoshift: A distribution shift benchmark for unsupervised anomaly detection","volume":"35","author":"dragoi","year":"2022","journal-title":"Advances in neural information processing systems"},{"year":"2013","author":"goodfellow","article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","key":"ref6"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/ICCV48922.2021.00814"}],"event":{"name":"2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)","start":{"date-parts":[[2023,7,13]]},"location":"Hong Kong, China","end":{"date-parts":[[2023,7,15]]}},"container-title":["2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10208269\/10208123\/10208545.pdf?arnumber=10208545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T17:46:27Z","timestamp":1693244787000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10208545\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/cacre58689.2023.10208545","relation":{},"subject":[],"published":{"date-parts":[[2023,7]]}}}