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Structure guided GANs
Cao FD(曹飞道); Zhao HC(赵怀慈); Liu PF(刘鹏飞)
作者部门光电信息技术研究室
会议名称LIDAR IMAGING DETECTION AND TARGET RECOGNITION 2017
会议日期July 23-25, 2017
会议地点Changchun, China
会议主办者Chinese Society for Optical Engineering (CSOE)
会议录名称Proceedings SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017
出版者SPIE
出版地Bellingham, USA
2017
页码1-4
收录类别EI ; CPCI(ISTP)
EI收录号20181705046916
WOS记录号WOS:000426279000097
产权排序1
ISSN号0277-786X
关键词Gans Structure Ambiguous Structure Similar
摘要Generative adversarial networks (GANs) has achieved success in many fields. However, there are some samples generated by many GAN-based works, whose structure is ambiguous. In this work, we propose Structure Guided GANs that introduce structural similar into GANs to overcome the problem. In order to achieve our goal, we introduce an encoder and a decoder into a generator to design a new generator and take real samples as part of the input of a generator. And we modify the loss function of the generator accordingly. By comparison with WGAN, experimental results show that our proposed method overcomes largely sample structure ambiguous and can generate higher quality samples.
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21305
专题光电信息技术研究室
通讯作者Zhao HC(赵怀慈)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016,P.R. China
2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China
3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang, Liaoning 110016, P. R. China
4.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016,P. R. China
推荐引用方式
GB/T 7714
Cao FD,Zhao HC,Liu PF. Structure guided GANs[C]//Chinese Society for Optical Engineering (CSOE). Bellingham, USA:SPIE,2017:1-4.
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