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

Xingrun Xing; Dawei Zhang

doi:10.1117/12.2540182

14 August 2019 Image-to-image translation using a relativistic generative adversarial network

Xingrun Xing, Dawei Zhang

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790I (2019) https://doi.org/10.1117/12.2540182
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Xingrun Xing and Dawei Zhang "Image-to-image translation using a relativistic generative adversarial network", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790I (14 August 2019); https://doi.org/10.1117/12.2540182

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KEYWORDS

Image quality

Image segmentation

Computer programming

Computer vision technology

Image processing

Image restoration

Machine vision

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