@inproceedings{f5c84bcb36684995950c84076f88fd67,
title = "Fast style transfer for Chinese tranditional ink painting",
abstract = "By style transfer mechanism, the style of another image can be illustrated toward another target picture. However, calculating speed and quality are two tricky issues. In this paper, we propose an accelerated version of the transfer for Chinese traditional ink painting style. By reducing the size of the rendering network, the training speed can be increased to 1.27 times. It is about the similar quality as the image generated by the original model. This can also reflect the characteristics of ink painting: Light ink, black and white.",
keywords = "Chinese Traditional Painting, Deep Learning, Style Transfer",
author = "Renjie Zhou and Han, \{Jeong Hoon\} and Yang, \{Hyeon Seok\} and Woojin Jeong and Moon, \{Young Shik\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019 ; Conference date: 12-07-2019 Through 14-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ICEIEC.2019.8784632",
language = "English",
series = "ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "586--588",
editor = "Wenzheng Li and Guomin Zuo",
booktitle = "ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication",
}