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Image-to-image translation using a relativistic generative adversarial network
Author(s): Xingrun Xing; Dawei Zhang
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Paper Abstract

The image-to-image translation tasks have improved a lot since the utilization of conditional generative adversarial network. However, GAN itself have the shortcoming in the training stability. Recent years, there are many new techniques to solve this problem and the relativistic generative adversarial network has modified a key element missing from standard GAN. In this paper, we will propose an improved image-to-image translation using a relativistic generative adversarial network which is easy to converge. This discriminator will not aim to find the fake data from the real directly, but to detect the faker ones. In the implementation, we take experiments in three image translation tasks which indicate the relativistic generative adversarial network have a universal applicability for this task. And our framework has achieved successful in the label-to-photo and photo-to-map tasks in experiments.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790I (14 August 2019); doi: 10.1117/12.2540182
Show Author Affiliations
Xingrun Xing, Zhengzhou Univ. (China)
Dawei Zhang, Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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