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Structure guided GANs
Cao FD(曹飞道); Zhao HC(赵怀慈); Liu PF(刘鹏飞)
Conference DateJuly 23-25, 2017
Conference PlaceChangchun, China
Author of SourceChinese Society for Optical Engineering (CSOE)
Source PublicationProceedings SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017
Publication PlaceBellingham, USA
Indexed ByEI ; CPCI(ISTP)
EI Accession number20181705046916
WOS IDWOS:000426279000097
Contribution Rank1
KeywordGans Structure Ambiguous Structure Similar
AbstractGenerative 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.
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Document Type会议论文
Corresponding AuthorZhao HC(赵怀慈)
Affiliation1.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
Recommended Citation
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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