Color and style transfer using Generative Adversarial Networks

dc.contributor.author	Kusyy, Andriy
dc.date.accessioned	2019-02-19T14:37:22Z
dc.date.available	2019-02-19T14:37:22Z
dc.date.issued	2019
dc.identifier.citation	Kusyy, Andriy. Color and style transfer using Generative Adversarial Networks : Master Thesis : manuscript / Andriy Kusyy ; Supervisor Dr. Rostyslav Hryniv ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2019. – 30 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1331
dc.language.iso	en	uk
dc.subject	Generative Adversarial Networks	uk
dc.subject	Dataset and preprocessing	uk
dc.subject	Generative Model	uk
dc.title	Color and style transfer using Generative Adversarial Networks	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	In this work, we present an end-to-end solution for an image to image color and style transfer using Conditional Generative Adversarial Networks. Nowadays photo editing industry is growing rapidly, and one of the crucial issues is recoloring and restyling of individual objects or areas on images. With a fast advancement of deep segmentation models, getting a precise segmentation mask for an area on a picture is no longer a problem although unsupervised restyling and recoloring of the object with complex patterns is still a challenge. The proposed model is a state-of-the-art regarding visual appearance and provides high structural similarity.	uk