Staranje obrazov s pomočjo globokih generativnih nevronskih mrež : magistrsko delo

Vesel, Nejc (author), Peer, Peter (mentor), Meden, Blaž (comentor), Štruc, Vitomir (comentor)

Staranje obrazov je področje, ki se ukvarja z modeliranjem staranja osebe iz ene same referenčne slike. Želimo ustvariti generativni model, ki nam s pomočjo nevronskih mrež ustvari slike referenčne osebe pri različnih starostnih skupinah. Pri našem pristopu smo želeli cilj doseči z uporabo različnih generativnih arhitektur. Preizkusili smo nekaj uveljavljenih pristopov ter implementirali nekaj lastnih idej, ki se niso izkazale za najuspešnejše. Dobljeni končni rezultati so bili pod pričakovanji, vendar naloga naredi pregled nad preizkušenimi pristopi in njihovo implementacijo. Naloga predstavlja dobro podlago za nadaljnje raziskovanje na tem področju, saj naredi pregled nad uspešnimi in neuspešnimi pristopi ter težavami, ki se pojavljajo pri raziskovanju tega področja.

• Language: Slovenian
• Keywords: staranje obrazov, variacijski avtoenkoder, generativne mreže, nevronske mreže, generativne nasprotniške mreže, nasprotniški avtoenkoder
• Work type: Master's thesis/paper
• Typology: 2.09 - Master's Thesis
• Organization: FMF - Faculty of Mathematics and Physics; FRI - Faculty of Computer and Information Science
• Year: 2019
• PID: 20.500.12556/RUL-106110
• UDC: 004
• COBISS.SI-ID: 18607449
• Publication date in RUL: 28.01.2019

VESEL, Nejc, 2019, Staranje obrazov s pomočjo globokih generativnih nevronskih mrež : magistrsko delo [online]. Master’s thesis. [Accessed 14 April 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=106110

Secondary language

• Language: English
• Title: Face aging using deep generative neural networks

Abstract

Face aging as a research topic is dealing with modelling human aging from a reference photo. We want a generative model that, using generative neural networks, generates images of a reference person at a different age. We implemented some existing approaches and developed some of our own, however, they didn't return results that we wished for. The final results were below expectations, however, the thesis makes a good overview over the implemented approaches and their implementation. The thesis creates a good foundation for further research. It gives a good overview over successful and non successful approaches and the difficulties that arise when doing research on this topic.

• Keywords: face aging, variational autoencoder, generative networks, neural networks, generative adverserial networks, adverserial autoencoders