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Proceedings Paper • new

Structure guided GANs
Author(s): Feidao Cao; Huaici Zhao; Pengfei Liu
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Paper Abstract

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.

Paper Details

Date Published: 15 November 2017
PDF: 4 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052U (15 November 2017); doi: 10.1117/12.2294482
Show Author Affiliations
Feidao Cao, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Huaici Zhao, Shenyang Institute of Automation (China)
Pengfei Liu, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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