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master's thesis
Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:000802
Jelavić, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Jelavić, M. (2017). Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:000802

Jelavić, Marko. "Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:000802

Jelavić, Marko. "Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:000802

Jelavić, M. (2017). 'Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 14 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:000802

Jelavić M. Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2025 January 14] Available at: https://urn.nsk.hr/urn:nbn:hr:168:000802

M. Jelavić, "Polunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:000802

Metadata
TitlePolunadzirana klasifikacija rukom pisanih znakova generativnim suparničkim modelima
Title (english)Semi-supervised classification of hand-written characters by generative adversarial networks
AuthorMarko Jelavić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarin Golub (član povjerenstva)
Committee memberAlan Jović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-20, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Klasifikacija slike je važno područje računalnog vida s velikim brojem zanimljivih primjena. Najbolji rezultati se postižu strogo nadziranim učenjem, no ručno označavanje podataka je izrazito skup proces. Kako bi smo smanjili potrebu za označavanjem služimo se polunadziranim učenjem, odnoso učenjem na djelomično označenim podacima. U ovom pristupu manji broj podataka je označen dok je većina neoznačena. Rad prezentira generativne suparničke modele koji se mogu koristiti u polunadzirane svrhe te razne implementacije istih i rezultate nad više skupova podataka.
Abstract (english)
Image classification is an important field of computer vision with many interesting applications. Best results are often accomplished with supervised learning, but manually labeling the data set is an expensive process. To lower the need for labeling big data sets, we use semi-supervised learning, which is basically learning on a partially labeled datas et. Smaller number of examples is labeled whereas bigger part of the data set is unlabeled. This thesis presents generative adversarial models which can be used in a semi-supervised setting along with many implementations of the aforementioned model. It also presents various implementations of the model and the experimental results on multiple data sets.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:000802
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-13 21:11:24