prikaz prve stranice dokumenta Adaptacija domene za semantičku segmentaciju slika
No public access
master's thesis
Adaptacija domene za semantičku segmentaciju slika
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:229972
Bertol, Leon
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Bertol, L. (2023). Adaptacija domene za semantičku segmentaciju slika (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:229972

Bertol, Leon. "Adaptacija domene za semantičku segmentaciju slika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:229972

Bertol, Leon. "Adaptacija domene za semantičku segmentaciju slika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:229972

Bertol, L. (2023). 'Adaptacija domene za semantičku segmentaciju slika', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:229972

Bertol L. Adaptacija domene za semantičku segmentaciju slika [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:229972

L. Bertol, "Adaptacija domene za semantičku segmentaciju slika", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:229972

Metadata
TitleAdaptacija domene za semantičku segmentaciju slika
Title (english)Domain adaptation for semantic segmentation
AuthorLeon Bertol
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberLeonardo Jelenković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-14, Croatia
Abstract
U ovom radu demonstrirali smo dvije različite metode za adaptaciju domene za semantičku segmentaciju prometnih scena. Kao izvorišne, odnosno sintetičke podatke za treniranje, koristili smo skup podataka dobivenih iz GTA5 i CARLA smiulatora. Ciljana domena, odnosno ona nad kojom smo testirali modele, čini skup podataka Cityscapes. Dok prva metoda suparničkim učenjem nastoji ciljanu domenu prilagoditi izvorišnoj, druga metoda prilagođava sam model kako bi dobro generalizirao na ciljanoj domeni koristeći tehnike samostalnog učenja. Kako bismo ispitali i bolje razumjeli što sve ima utjecaj na adaptaciju domene, isprobali smo različite arhitekture, hiperparametre te dopunske metode i usporedili dobivene rezultate.
Abstract (english)
In this paper, we demonstrated two different methods for domain adaptation for semantic segmentation of traffic scenes. As source, or synthetic data for training, we used a set of data obtained from GTA5 and CARLA simulators. The target domain, that is, the one on which we tested the models, is the dataset Cityscapes. While the first method tries to adapt the target domain to the original by adversarial learning, the second method adapts the model itself to generalize well on the target domain using self-learning techniques. In order to examine and better understand what influences domain adaptation, we tried different architectures, hyperparameters and supplementary methods and compared the obtained results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:229972
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
Terms of useLicence In copyright icon
Public note
Created on2023-10-11 08:26:06