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master's thesis
Primjena maskirajućih autoenkodera za semantičku segmentaciju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:211716
Merćep, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Merćep, L. (2023). Primjena maskirajućih autoenkodera za semantičku segmentaciju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:211716

Merćep, Luka. "Primjena maskirajućih autoenkodera za semantičku segmentaciju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:211716

Merćep, Luka. "Primjena maskirajućih autoenkodera za semantičku segmentaciju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:211716

Merćep, L. (2023). 'Primjena maskirajućih autoenkodera za semantičku segmentaciju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:168:211716

Merćep L. Primjena maskirajućih autoenkodera za semantičku segmentaciju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:211716

L. Merćep, "Primjena maskirajućih autoenkodera za semantičku segmentaciju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:211716

Metadata
TitlePrimjena maskirajućih autoenkodera za semantičku segmentaciju
Title (english)Application of masked autoencoders for semantic segmentation
AuthorLuka Merćep
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberTomislav Hrkać (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-11, Croatia
Abstract
Detekcija anomalija područje je istraživanja sa primjenama u brojnim područjima. Modeli bazirani na transformerima trenutno su stanje tehnike u području obrade prirodnog jezika. Ovaj rad u detekciji anomalija koristi model maskirnog autoenkodera koji je baziran na arhitekturi vizualnog transformera. Vizualni transformer je model s minimalnim izmjenama nad arhitekturom modela za primjene transformera na slikama. U ovom radu korišten je skup podataka, za detekciju anomalija u industrijskim procesima, MVTec AD za učenje i evaluaciju modela. Cilj rada bio je ispitati efikasnost modela baziranog na arhitekturi maskirnog autoenkodera u području detekcije anomalija.
Abstract (english)
Anomaly detection is a field of research with applications in numerous areas. Models based on transformers are the current state of the art in the field of natural language processing. This work in anomaly detection uses the masked autoencoder model, which is based on the visual transformer architecture. The visual transformer is a model with minimal changes to the model architecture for transformer applications in images. In this work, a set of data, for the detection of anomalies in industrial processes, MVTec AD was used for model learning and evaluation. The aim of the work was to examine the effectiveness of the model based on the masked autoencoder architecture in the area of anomaly detection.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:211716
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 on2023-10-11 09:48:12