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dc.contributor.authorAli-Gombe, Adamu
dc.contributor.authorElyan, Eyad
dc.contributor.authorSavoye, Yann
dc.contributor.authorJayne, Chrisina
dc.date.accessioned2018-04-30T09:04:52Z
dc.date.available2018-04-30T09:04:52Z
dc.identifier.citationALI-GOMBE, A., ELYAN, E., SAVOYE, Y. and JAYNE, C. 2018. Few-shot classifier GAN. Presented at the International joint conference on neural networks 2018 (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil.en
dc.identifier.issn2161-4393en
dc.identifier.issn2161-4407en
dc.identifier.urihttp://hdl.handle.net/10059/2881
dc.description.abstractFine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for few-shot classification. We address the problem of few-shot classification by designing a GAN in which the discriminator and the generator compete to output labeled data in any case. In contrast to previous methods, our techniques generate then classify images into multiple fake or real classes. A key innovation of our adversarial approach is to allow fine-grained classification using multiple fake classes with semi-supervised deep learning. A major strength of our techniques lies in its label-agnostic characteristic, in the sense that the system handles both labeled and unlabeled data during training. We validate quantitatively our few-shot classifier on the MNIST and SVHN datasets by varying the ratio of labeled data over unlabeled data in the training set. Our quantitative analysis demonstrates that our techniques produce better classification performance when using multiple fake classes and larger amount of unlabelled data.en
dc.language.isoengen
dc.publisherIEEEen
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0en
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectImage classificationen
dc.subjectFew-shot classificationen
dc.subjectGenerative adversarial networksen
dc.titleFew-shot classifier GAN.en
dc.typeConference publicationsen
dcterms.dateAccepted2018-04-22en
refterms.accessExceptionNAen
refterms.dateDeposit2018-04-30en
refterms.dateEmbargoEnd2018-07-20en
refterms.dateFCA2018-07-20en
refterms.dateFCD2018-04-30en
refterms.dateFreeToDownload2018-07-20en
refterms.dateFreeToRead2018-07-20en
refterms.dateToSearch2018-07-20en
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAMen


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