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Title:Uporaba generativnih nasprotniških nevronskih mrež za kreacijo slik psov : bachelor's thesis
Authors:ID Boršič, Teja (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf UN_Borsic_Teja_2021.pdf (1,73 MB)
MD5: 9FA508B109F58CBCF57A7B754E0980EA
PID: 20.500.12556/dkum/03248a1d-1b25-4f64-aff0-7f44bb5bf66e
 
Language:English
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Kot glavni cilj diplomske naloge smo poleg pregleda obstoječe literature preučili že razvit model generativne nasprotniške mreže StyleGAN ter podrobneje analizirali njeno implementacijo in delovanje. S pridobljenim znanjem iz literature in pregledom obstoječih rešitev smo izbrali projekt, ki smo ga prilagodili in optimizirali za delo na izbranih digitalnih slikah psov. Poudarili smo pomen uporabe ustreznih slik psov za učenje in vpliv različnih parametrov, kot so velikost serije, plasti in velikost slike, na optimizacijo zmogljivosti modela za ustvarjanje realističnih slik.
Keywords:nevronske mreže, psi, generacija slik, generativne nasprotniške mreže, StyleGAN
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Boršić]
Year of publishing:2021
Number of pages:IX, 34 str.
PID:20.500.12556/DKUM-80572 New window
UDC:004.8:004.93(043.2)
COBISS.SI-ID:86218499 New window
Publication date in DKUM:04.11.2021
Views:1400
Downloads:124
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:20.09.2021

Secondary language

Language:Slovenian
Title:Using generative adverserial neural networks for creation of dog images
Abstract:As our main objective, in addition to reviewing the existing literature, we studied an already developed model of the generative adversarial network StyleGAN, analyzed its implementation and operation in more detail. With the acquired knowledge from the literature and a review of existing solutions, we adapted and optimized a chosen project to work on our selected digital images of dogs. We captured the importance of using appropriate images of dogs for learning and the impact of different parameters, such as batch size, layers, and image size, on the optimization of the performance of the model for image generation. We successfully generated multiple realistic samples of dogs.
Keywords:neural networks, dogs, image generation, generative adversarial neural networks, StyleGAN


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