Doing more with less: similarity neural nets and metrics for small class imbalanced data sets Veal, Charlie, Schulz, Jeffrey, Buck, Andrew, Anderson, Derek, Keller, James, Popescu, Mihail, Scott, Grant, Ho, Dominic and Wilkin, Timothy 2020, Doing more with less: similarity neural nets and metrics for small class imbalanced data sets, in Proceedings of SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, SPIE, Bellingham, Washington, pp. 1141802-1-1141802-12, doi: 10.1117/12.2558092. Attached Files Name Description MIMEType Size Downloads Title Doing more with less: similarity neural nets and metrics for small class imbalanced data sets Author(s) Veal, Charlie Schulz, Jeffrey Buck, Andrew Anderson, Derek Keller, James Popescu, Mihail Scott, Grant Ho, Dominic Wilkin, Timothy orcid.org/0000-0003-4059-1354 Conference name Society of Photo-Optical Instrumentation Engineers. Conference (2020 : Online Only, United States) Conference location Online Only, United States Conference dates 2020/04/27 - 2020/05/08 Title of proceedings Proceedings of SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV Editor(s) Bishop, Steven S Isaacs, Jason C Publication date 2020 Series Society of Photo-Optical Instrumentation Engineers Conference Start page 1141802-1 End page 1141802-12 Total pages 12 Publisher SPIE Place of publication Bellingham, Washington Keyword(s) artificial neural network siamese network triplet loss deep learning explosive hazard detection automatic target recognition similarity network ISBN 9781510636132 ISSN 0277-786X 1996-756X Language eng DOI 10.1117/12.2558092 Indigenous content off HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30140655 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 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, 06 Aug 2020, 09:38:05 EST Fri, 07 Aug 2020, 05:00:20 EST Fri, 07 Aug 2020, 06:03:40 EST Tue, 01 Sep 2020, 12:18:48 EST Tue, 01 Sep 2020, 12:22:07 EST Sat, 09 Jan 2021, 01:42:34 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 77 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Thu, 06 Aug 2020, 09:38:05 EST

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