Painting style transfer with deep neural networks

Aljoša Rakita (2018) Painting style transfer with deep neural networks. EngD thesis.

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Abstract

What are deep neural networks and how they work is explained first in this diploma thesis. Next, we describe what is a painting style and what determines the style. With deep neural networks one can transfer the painting style from a selected source image to another target image. We demonstrate style transfer by making ``fake’’ pictures using paintings of Slovene impressionist painters. At the end we made a web survey to find out, if randomly selected people could distinguish such ``fake’’ pictures from the real painting of slovenian impressionists.

Item Type:

Thesis (EngD thesis)

Keywords:

style transfer, deep neural network, analysis

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
prof. dr. Franc Solina	71	Mentor
viš. pred. dr. Borut Batagelj	298	Comentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537952963)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

4241

Date Deposited:

18 Sep 2018 17:09

Last Modified:

04 Oct 2018 13:03

URI:

http://eprints.fri.uni-lj.si/id/eprint/4241

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