Extending deep learning to new classes without retraining

Schulz, Jeffrey, Veal, Charlie, Buck, Andrew, Anderson, Derek, Keller, James, Popescu, Mihail, Scott, Grant, Ho, Dominic KC and Wilkin, Timothy 2020, Extending deep learning to new classes without retraining, in Proceedings of SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, SPIE, Washington, D.C., pp. 1141803-1-1141803-14, doi: 10.1117/12.2558134.

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Title Extending deep learning to new classes without retraining
Author(s) Schulz, Jeffrey
Veal, Charlie
Buck, Andrew
Anderson, Derek
Keller, James
Popescu, Mihail
Scott, Grant
Ho, Dominic KC
Wilkin, TimothyORCID iD for 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 1141803-1
End page 1141803-14
Total pages 14
Publisher SPIE
Place of publication Washington, D.C.
Keyword(s) convolutional neural network
explosive hazard detection
EHD
automatic target recognition
ATR
possibilistic K nearest-neighbor
PKNN
clustering
clustering in ordered dissimilarity data
CLODD
ISBN 9781510636132
ISSN 0277-786X
1996-756X
Language eng
DOI 10.1117/12.2558134
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30140658

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