Mr. Xingrun Xing Profile

Xingrun Xing

at Zhengzhou Univ

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Proceedings Article | 14 August 2019 Paper

Image-to-image translation using a relativistic generative adversarial network

Xingrun Xing, Dawei Zhang

Proceedings Volume 11179, 111790I (2019) https://doi.org/10.1117/12.2540182

KEYWORDS: Image quality, Image segmentation, Machine vision, Computer vision technology, Computer programming, Image restoration, Image processing

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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.

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