You're currently viewing an old version of this dataset. To see the current version, click here.

FCSR-GAN: End-to-end learning for joint face completion and super-resolution

The proposed FCSR-GAN uses compound generator and carefully designed losses (adversarial loss, perceptual loss, smooth loss, pixel loss, and face parsing loss) to assure the quality of the recovered face images.

Data and Resources

This dataset has no data

Cite this as

Jiancheng Cai, Hu Han, Shiguang Shan, Xilin Chen (2025). Dataset: FCSR-GAN: End-to-end learning for joint face completion and super-resolution. https://doi.org/10.57702/4qzud31c

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
Created	January 2, 2025
Last update	January 2, 2025
Defined In	https://doi.org/10.48550/arXiv.1911.01045
Author	Jiancheng Cai
More Authors	Hu Han Shiguang Shan Xilin Chen
Homepage	https://github.com/swordcheng/FCSR-GAN