Adversarial image generation by combining content and style | |
Liu, Songyan1,2; Zhao, Chaoyang1,2; Gao, Yunze1,2; Wang, Jinqiao1,2; Tang, Ming1,2 | |
刊名 | IET IMAGE PROCESSING
![]() |
2019-12-12 | |
卷号 | 13期号:14页码:2716-2723 |
关键词 | image recognition feature extraction learning (artificial intelligence) image texture adversarial image generation unique style reference images style feature extraction module style specific image generation model double-cycle training strategy natural-content pairs input natural images style exchange style-exchanged images licence-plate image handbags images |
ISSN号 | 1751-9659 |
DOI | 10.1049/iet-ipr.2019.0103 |
英文摘要 | Images can be considered as the combination of two parts: the content and the style. The authors' approach can leverage this property by extracting a certain unique style from the reference images and combining it to generate images with new contents. With a well-defined style feature extraction module, they propose a novel framework to generate images with various styles and the same content. To train the style specific image generation model efficiently, a double-cycle training strategy is proposed: they input two natural-content pairs simultaneously, extract their style features, and exchange them twice to obtain the reconstruction of the input natural images. What is more, they apply the triplet margin loss to the style feature extracted from the images before and after style exchange and an adversarial discriminator to force the style-exchanged images to be real. They perform experiments on licence-plate image, Chinese characters, and shoes or handbags images generating, obtain photo-realistic results and remarkably improve the corresponding supervised recognition task. |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | INST ENGINEERING TECHNOLOGY-IET |
WOS记录号 | WOS:000505048400008 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/29473] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Zhao, Chaoyang |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Songyan,Zhao, Chaoyang,Gao, Yunze,et al. Adversarial image generation by combining content and style[J]. IET IMAGE PROCESSING,2019,13(14):2716-2723. |
APA | Liu, Songyan,Zhao, Chaoyang,Gao, Yunze,Wang, Jinqiao,&Tang, Ming.(2019).Adversarial image generation by combining content and style.IET IMAGE PROCESSING,13(14),2716-2723. |
MLA | Liu, Songyan,et al."Adversarial image generation by combining content and style".IET IMAGE PROCESSING 13.14(2019):2716-2723. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论