Ayni Şartlar Altinda Farkli Üretici Çekişmeli A?larin Karşilaştirilmasi

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dc.authorscopusid57207451467
dc.authorscopusid35732416100
dc.contributor.authorAltun S.
dc.contributor.authorTalu M.F.
dc.date.accessioned2024-08-04T20:04:00Z
dc.date.available2024-08-04T20:04:00Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 -- 11 October 2019 through 13 October 2019 -- 156063en_US
dc.description.abstractAs the first successful general purpose way of generating new data, GANs have shown great potential for a wide range of practical applications (including those in the fields of art, fashion, medicine and finance). It is one of the most popular research topics of recent times. GANs are the new class of exciting machine learning model that leads to applications that bring to mind their ability to produce synthetic but realistic looking data. Generative Adversarial Networks are composed of two neural networks that work in opposite directions. In this paper, it is aimed to examine the same initial situation, same dataset, same number of iterations, parts of the same size in order to compare Generative Adversarial Networks. This paper Generative Adversarial Network (GAN), Deconvolusional Generative Adversarial Network (DCGAN), Semi-Supervised Generative Adversarial Network (SGAN/SeGAN) Conditional Generative Adversarial Network (CoGAN / CGAN) were used. These methods were calculated on the performance of MNIST dataset. The results are presented both numerically and visually. © 2019 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT.2019.8932786
dc.identifier.isbn9781728137896
dc.identifier.scopus2-s2.0-85078049333en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT.2019.8932786
dc.identifier.urihttps://hdl.handle.net/11616/92252
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCGANen_US
dc.subjectDCGANen_US
dc.subjectGANen_US
dc.subjectMNISTen_US
dc.subjectSGANen_US
dc.titleAyni Şartlar Altinda Farkli Üretici Çekişmeli A?larin Karşilaştirilmasien_US
dc.typeConference Objecten_US

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