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master's thesis
Generativno modeliranje uvjetnim normalizirajućim tokovima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:164072
Ćosić, Tomislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Ćosić, T. (2024). Generativno modeliranje uvjetnim normalizirajućim tokovima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:164072

Ćosić, Tomislav. "Generativno modeliranje uvjetnim normalizirajućim tokovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:164072

Ćosić, Tomislav. "Generativno modeliranje uvjetnim normalizirajućim tokovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:164072

Ćosić, T. (2024). 'Generativno modeliranje uvjetnim normalizirajućim tokovima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:164072

Ćosić T. Generativno modeliranje uvjetnim normalizirajućim tokovima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:164072

T. Ćosić, "Generativno modeliranje uvjetnim normalizirajućim tokovima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:164072

Metadata
TitleGenerativno modeliranje uvjetnim normalizirajućim tokovima
Title (english)Generative modelling with conditional normalizing flows
AuthorTomislav Ćosić
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
Committee memberAdrian Satja Kurdija (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-10, Croatia
Abstract
Procjena gustoće i generiranje slika važni su zadatci računalnog vida s mnogim zanimljivim primjenama. Poznato je da generativni modeli slika ne uspijevaju naučiti semantičke koncepte. Ovaj problem možemo ublažiti uvjetovanjem generativnog modela semantičkim informacijama. U okviru rada, potrebno je odabrati okvir za automatsku diferencijaciju te upoznati biblioteke za rukovanje tenzorima i slikama. Proučiti i ukratko opisati postojeće generativne arhitekture. Odabrati slobodno dostupne skupove slika te oblikovati podskupove za učenje, validaciju i testiranje. Oblikovati uvjetni generativni tok te uhodati postupke učenja i validiranja hiperparametara. Primijeniti naučene modele, prikazati eksperimente na javno dostupnim podatcima te usporediti generalizacijsku izvedbu sa stanjem tehnike. Komentirati učinkovitost učenja i zaključivanja. Predložiti pravce za budući rad. Radu priložiti izvorni i izvršni kod razvijenih postupaka, ispitne slijedove i rezultate, uz potrebna objašnjenja i dokumentaciju. Citirati korištenu literaturu i navesti dobivenu pomoć.
Abstract (english)
Density estimation and image generation are important tasks in computer vision with many interesting applications. It is known that image generative models fail to learn semantic concepts. This problem can be mitigated by conditioning the generative model using semantic information. In the scope of the paper, it is required to choose an automatic differentiation framework and become familiar with libraries used to handle tensors and images. Research and briefly describe existing generative architectures. Choose freely available image dataset and construct subsets for training, validation and testing. Model a conditional generative flow and implement training and hyperparameter validation. Apply trained models, perform experiments on publicly available datasets and compare generative performance with state-of-the-art. Comment on training and inference efficiency. Suggest direction for future work. Attach source code and executable code of developed processes to the paper, experiments and results, along with necessary explanations and documentation. Cite used literature and state received help.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:164072
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 on2024-10-12 20:22:30