Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings Aslam, Masood, Khan, Tariq, Naqvi, Syed Saud, Holmes, Geoff and Naffa, Rafea 2020, Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings, IEEE Access, vol. 8, pp. 198600-198614, doi: 10.1109/access.2020.3034731. Attached Files Name Description MIMEType Size Downloads Title Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings Author(s) Aslam, Masood Khan, Tariq orcid.org/0000-0002-7477-1591 Naqvi, Syed Saud Holmes, Geoff Naffa, Rafea Journal name IEEE Access Volume number 8 Start page 198600 End page 198614 Total pages 15 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2020 ISSN 2169-3536 Keyword(s) Leather defects visual inspection convolutional neural networks transfer learning classification Language eng DOI 10.1109/access.2020.3034731 Indigenous content off Field of Research 08 Information and Computing Sciences 09 Engineering 10 Technology HERDC Research category C1.1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30145208 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 11 Nov 2020, 07:06:16 EST Thu, 12 Nov 2020, 04:00:54 EST Thu, 12 Nov 2020, 06:08:54 EST Thu, 12 Nov 2020, 12:15:02 EST Fri, 13 Nov 2020, 05:04:39 EST Fri, 13 Nov 2020, 11:20:37 EST Fri, 13 Nov 2020, 11:22:53 EST Sat, 09 Jan 2021, 01:42:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 49 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 12 Nov 2020, 12:15:02 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.