Improved face super-resolution generative adversarial networks-Reference-Cited by-同舟云学术

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Improved face super-resolution generative adversarial networks

Published:2020-04-05 Issue:4 Volume:31 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Wang Mengxue,Chen Zhenxue^ORCID,Wu Q. M. Jonathan,Jian Muwei

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

http://link.springer.com/content/pdf/10.1007/s00138-020-01073-6.pdf

Reference49 articles.

1. Jourabloo, A., Ye, M., Liu, X., Ren, L.: Pose-invariant face alignment with a single CNN. In: The IEEE International Conference on Computer Vision (ICCV) (2017)

2. Tzimiropoulos, G.: Project-out cascaded regression with an application to face alignment. In IEEE Conference on Computer Vision & Pattern Recognition (CVPR) (2015)

3. Taigman, Y., Ming, Y., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision & Pattern Recognition (2014)

4. Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., Xu, Y.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 156–171 (2016)

5. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

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