不同纹理复杂度图像生成中CycleGAN循环一致损失系数优化选择方法

doi:10.11896／j.issn.1002-137X.2019.01.015

Abstract

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

CLC Number:

TP183

XU Qiang, ZHONG Shang-ping, CHEN Kai-zhi, ZHANG Chun-yang. Optimized Selection Method of Cycle-consistent Loss Coefficient of CycleGAN in Image Generation with Different Texture Complexity[J].Computer Science, 2019, 46(1): 100-106.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.jsjkx.com/EN/10.11896／j.issn.1002-137X.2019.01.015

https://www.jsjkx.com/EN/Y2019/V46/I1/100

References

[1]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2015:3431-3440. [2]GATYS L,ECKER A S,BETHGE M.Texture synthesis using convolutional neural networks[C]//Advances in Neural Information Processing Systems.Cambridge,Massachusetts:MIT Press,2015:262-270. [3]GATYS L A,ECKER A S,BETHGE M.Image style transfer using convolutional neural networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,NJ:IEEE,2016:2414-2423. [4]JOHNSON J,ALAHI A,FEI-FEI L.Perceptual losses for real-time style transfer and super-resolution[C]//European Con-ference on Computer Vision.Berlin,German:Springer,2016:694-711. [5]NASH J.Non-Cooperative Games.Annals of Mathematics,1951,54(2):286-295. [6]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//International Conference on Neural Information Processing Systems.Cambridge,Massachusetts:MIT Press,2014:2672-2680. [7]DENTON E L,CHINTALA S,FERGUS R.Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[C]//Advances in Neural Information Processing Systems.Cambridge,Massachusetts:MIT Press,2015:1486-1494. [8]LI C,WAND M.Precomputed real-time texture synthesis with markovian generative adversarial networks[C]//European Conference on Computer Vision.Berlin,German:Springer,2016:702-716. [9]LI C,ZHAO X Y,XIAO L M,et al.Multi-layer perceptual defogging algorithm for image under generative adversarial networks[J].Journal of Computer-Aided Design & Computer Graphics,2017,29(10):1835-1843.(in Chinese) 李策,赵新宇,肖利梅,等.生成对抗映射网络下的图像多层感知去雾算法[J].计算机辅助设计与图形学学报,2017,29(10):1835-1843. [10]LEDIG C,WANG Z,SHI W,et al.Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network//Computer Vision and Pattern Recognition.IEEE,2017:105-114. [11]MIRZA M,OSINDERO S.Conditional generative adversarial nets.arXiv preprint arXiv:1411.1784,2014. [12]ZHU J Y,KRÄHENBÜHL P,SHECHTMAN E,et al.Generative visual manipulation on the natural image manifold[C]//European Conference on Computer Vision.Berlin,German:Sprin-ger,2016:597-613. [13]ISOLA P,ZHU J Y,ZHOU T,et al.Image-to-Image Translation with Conditional Adversarial Networks[C]//IEEE Con-ference on Computer Vision and Pattern Recognition.Pisca-taway,NJ:IEEE,2017:5967-5976. [14]RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J].arXiv:1511.06434.2016. [15]FUKUSHIMA K.Neural network model for a mechanism of pattern recognition unaffected by shift in position-Neocognitron[J].IEICE Technical Report A,1979,62(10):658-665. [16]YI Z,ZHANG H,TAN P,et al.DualGAN:Unsupervised Dual Learning for Image-to-Image Translation[C]//IEEE International Conference on Computer Vision.Piscataway,NJ:IEEE,2017:2868-2876. [17]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein GAN[J].arXiv preprint arXiv:1701.07875,2017. [18]GOLDSTEIN T,OSHER S.The split Bregman method for L1-regularized problems[J].SIAM Journal on Imaging Sciences,2009,2(2):323-343. [19]KIM T,CHA M,KIM H,et al.Learning to Discover Cross-Domain Relations with Generative Adversarial Networks[C]//Proceedings of the 34th International Conference on Machine Learning.New York:ACM,2017:1857-1865. [20]ZHU J Y,PARK T,ISOLA P,et al.Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks[C]//IEEE International Conference on Computer Vision.Piscata-way,NJ:IEEE,2017:2242-2251. [21]HE D,XIA Y,QIN T,et al.Dual learning for machine translation[C]//Advances in Neural Information Processing Systems.Cambridge,Massachusetts:MIT Press,2016:820-828. [22]ZHOU T,KRAHENBUHL P,AUBRY M,et al.Learning dense correspondence via 3d-guided cycle consistency[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:117-126. [23]CARDACI M,DI GESÙ V,PETROU M,et al.A fuzzy approach to the evaluation of image complexity[J].Fuzzy Sets and Systems,2009,160(10):1474-1484. [24]RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252. [25]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:2818-2826. [26]WANG X,GUPTA A.Generative image modeling using style and structure adversarial networks[C]//European Conference on Computer Vision.Berlin,German:Springer,2016:318-335. [27]MAO X,LI Q,XIE H,et al.Least squares generative adversarial networks//2017 IEEE International Conference on ComputerVision (ICCV).IEEE,2017:2813-2821. [28]SABOUR S,FROSST N,HINTON G E.Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems.Berlin,German:MIT Press,2017:3859-3869. [29]LEEUWENBERG E,BUFFART H.An outline of coding theory[J].Advances in psychology,1983,11:25-47. [30]SU H,BOURIDANE A,CROOKES D.Scale Adaptive Complexity Measure of 2D Shapes//International Conference on Pattern Recognition.Piscataway,NJ:IEEE,2006:134-137. [31]PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on knowledge and data engineering,2010,22(10):1345-1359. [32]ZHOU B,LAPEDRIZA A,XIAO J,et al.Learning deep features for scene recognition using places database[C]//Advances in neural information processing systems.Cambridge,Massachusetts:MIT Press,2014:487-495. [33]GARCIA B, BRUNET P.3D reconstruction with projective octrees and epipolar geometry//International Conference on Computer Vision.Piscataway,NJ: IEEE,2008:1067-1072.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

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

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 8

Metrics

Comments

Recommended 0

[1]	SHI Da, LU Tian-liang, DU Yan-hui, ZHANG Jian-ling, BAO Yu-xuan. Generation Model of Gender-forged Face Image Based on Improved CycleGAN [J]. Computer Science, 2022, 49(2): 31-39.
[2]	TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[3]	CUI Wen-hao, JIANG Mu-rong, YANG Lei, FU Peng-ming, ZHU Ling-xiao. Combining MCycleGAN and RFCNN to Realize High Resolution Reconstruction of Solar Speckle Image [J]. Computer Science, 2021, 48(6A): 38-42.
[4]	ZHANG Yang, MA Xiao-hu. Anime Character Portrait Generation Algorithm Based on Improved Generative Adversarial Networks [J]. Computer Science, 2021, 48(1): 182-189.
[5]	YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[6]	XU Yong-shi, BEN Ke-rong, WANG Tian-yu, LIU Si-jie. Study on DCGAN Model Improvement and SAR Images Generation [J]. Computer Science, 2020, 47(12): 93-99.
[7]	ZHOU Bing, LIU Yu-xia, YANG Xin-xin, LIU Yang. Review of Research on Image Complexity [J]. Computer Science, 2018, 45(9): 30-37.
[8]	LI Qing and LI Dong-hui. Lossless Color Image Compression Method Based on Fuzzy Logic [J]. Computer Science, 2014, 41(Z11): 103-106.