Computer Science ›› 2019, Vol. 46 ›› Issue (1): 100-106.doi: 10.11896/j.issn.1002-137X.2019.01.015

• CCDM2018 • Previous Articles     Next Articles

Optimized Selection Method of Cycle-consistent Loss Coefficient of CycleGAN in Image Generation with Different Texture Complexity

XU Qiang, ZHONG Shang-ping, CHEN Kai-zhi, ZHANG Chun-yang   

  1. (College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China)
    (Network System Information Security Fujian Provincial University Key Laboratory,Fuzhou University,Fuzhou 350116,China)
  • Received:2018-05-11 Online:2019-01-15 Published:2019-02-25

Abstract: High-quality image generation has always been a difficult and hot topic in the field of computer vision and other exploration.CycleGAN achieves good results in unsupervised image generation tasks by using cycle-consistent losses.However,in face of image generation tasks with different texture complexity,CycleGAN’s cycle-consistent loss coefficient is unchanged by default,and its generated images have weak points such as texture distortion or even disappear,which can not guarantee the quality of generated images.In this paper,the complexity of image texture was mea-sured by integrating the spatial dimension and time dimension of images,the importance of cycle-consistent loss function in optimizing objective function was clarified,the correlation between the size of the cycle-consistent loss coefficient and the quality of image with different texture complexity was discovered and explained.The higher the texture complexity,the larger the cycle-consistent loss coefficient should be selected.Otherwise,the smaller coefficient should be taken.Using benchmarks and self-acquired image data sets,the classification accuracy based on migration learning was introduced to generate image quality assessment indicators.The experimental results show that the optimal choice of the appropriate cycle-consistent loss factor can effectively improve the quality of generated images.

Key words: Image generation, CycleGAN, Optimization of selection coefficient, Cycle-consistent loss, Texture complexity

CLC Number: 

  • TP183
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