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Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniques

dc.contributor.authorMorales, D.
dc.contributor.authorTalavera, E.
dc.contributor.authorRemeseiro López, Beatriz 
dc.date.accessioned2022-02-01T08:05:55Z
dc.date.available2022-02-01T08:05:55Z
dc.date.issued2021
dc.identifier.citationNeural Computing and Applications, 33, p. 16937-16949 (2021); doi:10.1007/s00521-021-06282-2
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/10651/61723
dc.descriptionVéase la corrección en la versión del editor.
dc.description.sponsorshipPartial financial support was received from HAT.tec GmbH. This work has been nancially supported in part by European Union ERDF funds, by the Spanish Ministry of Science and Innovation (research project PID2019-109238GB-C21), and by the Principado de Asturias Regional Government (research project IDI-2018-000176).
dc.format.extentp. 16937-16949
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.rights© The authors, under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
dc.rightsCC Reconocimiento - No Comercial - Sin Obra Derivada 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110281253&doi=10.1007%2fs00521-021-06282-2&partnerID=40&md5=e69d103cb53c791b672284b2102f8cc0
dc.titlePlaying to distraction: towards a robust training of CNN classifiers through visual explanation techniques
dc.typejournal article
dc.identifier.doi10.1007/s00521-021-06282-2
dc.relation.projectIDMICINN/PID2019-109238GB-C21
dc.relation.projectIDIDI-2018-000176
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s00521-021-06282-2
dc.rights.accessRightsopen access
dc.type.hasVersionSMUR


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© The authors, under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
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