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Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard

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dc.affiliationUniwersytet Warszawski
dc.contributor.authorChlebus, Marcin
dc.contributor.authorKorycki, Michał
dc.contributor.authorKruk, Michał
dc.contributor.authorWieczorek, Grzegorz
dc.contributor.authorJegorowa, Albina
dc.contributor.authorGajda, Janusz
dc.contributor.authorKontna, Kamila
dc.contributor.authorChyrowicz, Katarzyna
dc.date.accessioned2024-01-25T13:05:56Z
dc.date.available2024-01-25T13:05:56Z
dc.date.copyright2021-12-02
dc.date.issued2021
dc.description.accesstimeAT_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.number23
dc.description.versionFINAL_PUBLISHED
dc.description.volume21
dc.identifier.issn1424-8220
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/113053
dc.identifier.weblinkhttps://www.mdpi.com/1424-8220/21/23/8077#cite
dc.languageeng
dc.pbn.affiliationeconomics and finance
dc.relation.ispartofSensors
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.enmulti-class classification
dc.subject.enlaminated chipboard
dc.subject.enGAN
dc.subject.enCNN
dc.titleMulticlass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard
dc.typeJournalArticle
dspace.entity.typePublication