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Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks
Swedish Museum of Natural History, Department of Bioinformatics and Genetics.ORCID iD: 0000-0003-1093-2752
Savantic AB.
Kungliga tekniska högskolan.ORCID iD: 0000-0002-4266-6746
Charles University of Prague.ORCID iD: 0000-0002-3699-9245
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2019 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 68, p. 876-895Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2019. Vol. 68, p. 876-895
National Category
Biological Systematics
Research subject
Diversity of life
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URN: urn:nbn:se:nrm:diva-3670DOI: DOI:10.1093/sysbio/syz014OAI: oai:DiVA.org:nrm-3670DiVA, id: diva2:1381431
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-22Bibliographically approved

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Valan, MiroslavMaki, AtsutoVondracek, DominikRonquist, Fredrik
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