Application of Generative Neural Models for Style Transfer Learning in Fashion

dc.contributor.author	Mykhailych, Mykola
dc.date.accessioned	2018-01-25T18:24:33Z
dc.date.available	2018-01-25T18:24:33Z
dc.date.issued	2018
dc.identifier.citation	Mykhailych, Mykola. Application of Generative Neural Models for Style Transfer Learning in Fashion : Master Thesis : manuscript / Mykola Mykhailych ; Supervisor Dr. Rostyslav Hryniv ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2017. – 63 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1190
dc.description.abstract	The purpose of this thesis is to analyze different generative adversarial networks for application in fashion. Research of “mode collapse” problem of generative adversarial networks. We studied the theoretical part of the “mode collapse” and conducted experiments on a synthetic toy dataset, and a dataset containing real data from fashion. Due to the developed method, it was possible to achieve visible results of improving the quality of garment generation by solving the problem of collapse.	uk
dc.language.iso	en	uk
dc.subject	Generative modeling	uk
dc.subject	GAN framework	uk
dc.subject	Datasets	uk
dc.subject	Pix2pix framework	uk
dc.title	Application of Generative Neural Models for Style Transfer Learning in Fashion	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk