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Face Aging with Contextual Generative Adversarial Nets
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Most authors have used face ageing datasets consisting of photographs.
“Most authors have used face ageing datasets consisting of photographs, for example AGFW, CACD, FG-Net or MORPH [14–17].”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [15]
Both prototyping-based and modelling-based age progression approaches require face ageing sequences for the same person spanning a wide range of ages, which are very costly to collect.
“Both these approaches require face ageing sequences for the same person spanning a wide range of ages, which are very costly to collect [15].”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [15]
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
Energy-based Generative Adversarial Network
Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · cited 1×
“In future, we plan to employ other GANs to improve the performance, such as Wasserstein GAN [3], LS GAN [26], EB GANs [14].”
MethodFace swapping: automatically replacing faces in photographs
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360638 · Cited by 333
Flagged first, then by citations
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Sorted by citations
Khosravy, M., Nakamura, K., Hirose, Y., Nitta, N., & Babaguchi, N. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3140687 · Cited by 94
Koudelová, J., Hoffmannová, E., Dupej, J., & Velemínská, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212618 · Cited by 33
Metadata
- Authors
- Liu, S., Sun, Y., Zhu, D., Bao, R., Wang, W., Shu, X., & Yan, S.
- Affiliations
- Chinese Academy of Sciences, Beijing, China
- University of Trento, Italy, Trento, Italy
- Nanjing University of Science and Technology, Nanjing, China
- National University of Singapore, Beijing, China
- Venue
- Proceedings of the 25th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2017-10-19
- Pages
- 82-90
- Cited by
- 90
- References
- 6
- Funders
- Natural Science Foundation of Jiangsu Province (Grant No. BK20170856)
- National Natural Science Foundation of China (No.61572493, Grant U1536203)
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