Color and style transfer using Generative Adversarial Networks

Show simple item record

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


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search


Browse

My Account

Statistics