Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection Gong, Dong, Liu, Lingqiao, Le, Vuong, Saha, Budhaditya, Mansour, Moussa Reda, Venkatesh, Svetha and Van Den Hengel, Anton 2019, Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection, in ICCV 2019 : Proceedings of the IEEE International Conference on Computer Vision, IEEE, Pisctaway, N.J., pp. 1705-1714, doi: 10.1109/ICCV.2019.00179. Attached Files Name Description MIMEType Size Downloads Title Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection Author(s) Gong, Dong Liu, Lingqiao Le, Vuong orcid.org/0000-0003-1582-1269 Saha, Budhaditya orcid.org/0000-0001-8011-6801 Mansour, Moussa Reda Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Van Den Hengel, Anton Conference name Computer Vision. Conference (2019 : Seoul, South Korea) Conference location Seoul, South Korea Conference dates 27 Oct. - 2 Nov. 2019 Title of proceedings ICCV 2019 : Proceedings of the IEEE International Conference on Computer Vision Publication date 2019 Start page 1705 End page 1714 Total pages 10 Publisher IEEE Place of publication Pisctaway, N.J. ISBN 9781728148038 ISSN 1550-5499 Language eng DOI 10.1109/ICCV.2019.00179 Indigenous content off HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30135927 Document type: Conference Paper Collection: A2I2 (Applied Artificial Intelligence Institute) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 02 Apr 2020, 09:42:14 EST Fri, 03 Apr 2020, 04:01:05 EST Fri, 03 Apr 2020, 05:04:04 EST Fri, 08 May 2020, 13:05:30 EST Fri, 08 May 2020, 13:07:32 EST Fri, 08 May 2020, 13:08:06 EST Sat, 15 Aug 2020, 02:30:56 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 34 times in Scopus Search Google Scholar Access Statistics: 113 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 02 Apr 2020, 09:42:14 EST

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