Neural Style Transfer
Elmitwally, Nouh and Imtiaz, Talha and Saqib, Shazia (2022) Neural Style Transfer. ICNGC 2022 Conference Proceedings. pp. 241-244.
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ICNGC2022_FinalPaper_20.pdf - Accepted Version Download (324kB) |
Abstract
It is a very challenging task for image processing techniques to render the semantic contents of one image in different styles. For this, Neural Style Transfer (NST) is being used. NST is an application of Deep Neural Networks. The basic purpose of this paper is to help Textile industry and fashion industry using NST. The global apparel manufacturing market is a trillion $ market. Designing apparel is a major task and post COVID era all industry is struggling to minimize operational cost. We propose a neural network for style transfer, which can generate millions of stylized images using content and style images pair.
| Item Type: | Article | ||||||
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| Uncontrolled Keywords: | Convolutional Neural Network (CNN) Neural Style Transfer (NST) Variation Loss | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Nouh Elmitwally | ||||||
| Date Deposited: | 22 Nov 2022 11:29 | ||||||
| Last Modified: | 28 Nov 2022 16:13 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13750 |
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