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master's thesis
Regulariziranje normalizirajućih tokova suparničkim gubitkom
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:137523
Sršen, Martin
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Sršen, M. (2022). Regulariziranje normalizirajućih tokova suparničkim gubitkom (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:137523

Sršen, Martin. "Regulariziranje normalizirajućih tokova suparničkim gubitkom." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:137523

Sršen, Martin. "Regulariziranje normalizirajućih tokova suparničkim gubitkom." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:137523

Sršen, M. (2022). 'Regulariziranje normalizirajućih tokova suparničkim gubitkom', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:137523

Sršen M. Regulariziranje normalizirajućih tokova suparničkim gubitkom [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:137523

M. Sršen, "Regulariziranje normalizirajućih tokova suparničkim gubitkom", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:137523

Metadata
TitleRegulariziranje normalizirajućih tokova suparničkim gubitkom
Title (english)Regularization of normalizing flows with adversarial loss
AuthorMartin Sršen
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Generiranje slika važno je područje računalnog vida s mnogim zanimljivim primjenama. Posebno su zanimljivi modeli zasnovani na normalizirajućim tokovima jer istodobno nude i mogućnost procjene izglednosti i brzo generiranje. Ipak, kvaliteta generiranih slika nešto je lošija nego kod modela zasnovanih na suparničkom gubitku. Ovaj rad proučava hibridne algoritme pristupa koji bi pokušali objediniti dobre strane oba pristupa. U početku rada se uvodimo u osnove strojnog učenja, u odabrani okvir za automatsku diferencijaciju pyTorch te biblioteke za rukovanje tenzorima i slikama. Nakon toga detaljnije se upuštamo u vrste generativnih modela, njihove prednosti i nedostatke, razvoj kroz vrijeme. Na kraju iznosimo eksperimente i rezultate te sugestije za budući razvoj.
Abstract (english)
Image generation is an important area of ​​computer vision with many interesting applications. Particularly interesting are models based on normalizing flows because they offer the possibility of estimating the probability and fast generation at the same time. However, the quality of the generated images is slightly worse than that of models based on adversial loss. This paper examines hybrid algorithms of approaches that would attempt to combine the strengths of both approaches. At the beginning of the paper, we are introduced to the basics of machine learning, to the selected framework for automatic differentiation pyTorch and the libraries for handling tensors and images. After that, we go into more detail about the types of generative models, their advantages and disadvantages, and their development over time. Finally, we present the experiments and results and suggestions for further development.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:137523
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
Access conditionsClosed access
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Created on2023-01-13 16:08:12