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Image-to-Image Local Feature Translation Using Double Adversarial Networks Based on CycleGAN

  • Chen WuEmail author
  • Lei Li
  • Zhenzhen Yang
  • Peihong Yan
  • Jiali Jiao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

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.

Keywords

Local feature translation Generative adversarial networks Double adversarial CycleGAN 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Chen Wu
    • 1
    Email author
  • Lei Li
    • 1
  • Zhenzhen Yang
    • 1
  • Peihong Yan
    • 1
  • Jiali Jiao
    • 1
  1. 1.National Engineering Research Center of Communication and Network Technology School of ScienceNanjing University of Posts and TelecommunicationsNanjingChina

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