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master's thesis
Generiranje slika uvjetnim generativnim suparničkim mrežama
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:001177
Đidara, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Đidara, M. (2019). Generiranje slika uvjetnim generativnim suparničkim mrežama (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:001177

Đidara, Marko. "Generiranje slika uvjetnim generativnim suparničkim mrežama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:001177

Đidara, Marko. "Generiranje slika uvjetnim generativnim suparničkim mrežama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:001177

Đidara, M. (2019). 'Generiranje slika uvjetnim generativnim suparničkim mrežama', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:001177

Đidara M. Generiranje slika uvjetnim generativnim suparničkim mrežama [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:001177

M. Đidara, "Generiranje slika uvjetnim generativnim suparničkim mrežama", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:001177

Metadata
TitleGeneriranje slika uvjetnim generativnim suparničkim mrežama
Title (english)Generating Images Using Conditional Generative Adversarial Networks
AuthorMarko Đidara
MentorTomislav Hrkać (mentor)
Committee memberTomislav Hrkać (predsjednik povjerenstva)
Committee memberSiniša Šegvić (član povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog diplomskog rada je primijeniti generativne suparničke mreža za generiranje slika lica uz dane uvjete i time ispitati modele koji su prikladni za uspješno generiranje slika. Kroz početna poglavlja opisane su stavke dubokog učenja koje definiraju modele generativnih suparničkih mreža kao što jesu. Nakon upoznavanja terminologije i osnovnih dijelova GAN modela, opisan je primjer modela cDCGAN s pojednostavljenom arhitekturom generatora i diskriminatora, koji se koristio za generiranje slika brojeva MNIST baze podataka i za upoznavanje s radom uvjetnih GAN modela. Posljednja poglavlja opisuju modele cDCGAN i AC-DRAGAN koji su korišteni za generiranje slika lica. Ti modeli su trenirani na CelebA bazi podataka. Oba modela sadrže opis arhitekture generatora i diskriminatora, strukturiranje ulaznih podataka i rješenja koja su generirali ti modeli.
Abstract (english)
The goal of this master thesis is to apply a generative adversarial networks to generate images of human faces with given conditions and thereby examine models that are suitable for successful image generation. Through the initial chapters, deep learning elements are described which define the generative adversarial networks as they are. After addressing the terminology and basic parts of the GAN model, an example of the cDCGAN model with the simplified architecture of the generator and discriminator is described, which was used for generating images of numbers of the MNIST dataset and to learn how the conditional GAN model works. The last chapters describe cDCGAN and AC-DRAGAN models which are used for generating images of human faces. These models were trained on CelebA dataset. Both models contain a description of the architecture of the generator and discriminator, the structure of input data, and images that were generated by the models.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:001177
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 on2020-01-07 17:48:37