An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion

Chang Zhou, Zhenghong Gu, Yu Gao and Jin Wang
Additional contact information
Chang Zhou: College of Information Engineering, Yangzhou University, Yangzhou 225000, China
Zhenghong Gu: College of Information Engineering, Yangzhou University, Yangzhou 225000, China
Yu Gao: College of Information Engineering, Yangzhou University, Yangzhou 225000, China
Jin Wang: Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China

Sustainability, 2019, vol. 11, issue 20, 1-15

Abstract: Creation of art is a complex process for its abstraction and novelty. In order to create those art with less cost, style transfer using advanced machine learning technology becomes a popular method in computer vision field. However, traditional transferred image still troubles with color anamorphosis, content losing, and time-consuming problems. In this paper, we propose an improved style transfer algorithm using the feedforward neural network. The whole network is composed of two parts, a style transfer network and a loss network. The style transfer network owns the ability of directly mapping the content image into the stylized image after training. Content loss, style loss, and Total Variation (TV) loss are calculated by the loss network to update the weight of the style transfer network. Additionally, a cross training strategy is proposed to better preserve the details of the content image. Plenty of experiments are conducted to show the superior performance of our presented algorithm compared to the classic neural style transfer algorithm.

Keywords: style transfer; convolution neural network; cross training; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/20/5673/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/20/5673/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:20:p:5673-:d:276415

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().