Abstract
Image-to-image translation is a hot field in the machine learning with the emergency of the generative adversarial networks. Most of the latest models easily lead to changes in the overall image and overfitting when they are used to local feature translation. To address these limitations, this article adds a suppressor and proposes a double adversarial CycleGAN. The suppressor is added to suppress the change of images, and the suppressor and generator form a new adversarial relationship. We hope it will achieve Nash equilibrium that is the change of image focus on the local feature. Finally, a contrast experiment was conducted. In the case of image local feature transfer, the change of image is focused on the local features and the overfitting phenomenon can be well resolved.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 61501251, 61373137, 61071167) and the Science Foundation of Nanjing University of Posts and Telecommunications Grant (NY214191).
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Wu, C., Li, L., Yang, Z., Yan, P., Jiao, J. (2020). Image-to-Image Local Feature Translation Using Double Adversarial Networks Based on CycleGAN. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_109
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DOI: https://doi.org/10.1007/978-981-13-6504-1_109
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