Corpus ID: 29630552

Face Super-Resolution Through Wasserstein GANs

@article{Chen2017FaceST,
  title={Face Super-Resolution Through Wasserstein GANs},
  author={Zhimin Chen and Yuguang Tong},
  journal={ArXiv},
  year={2017},
  volume={abs/1705.02438}
}
Generative adversarial networks (GANs) have received a tremendous amount of attention in the past few years, and have inspired applications addressing a wide range of problems. Despite its great potential, GANs are difficult to train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al. 2017b; and Gulrajani et al. 2017) proposed using Wasserstein distance as the training objective and promised easy, stable GAN training across architectures with minimal hyperparameter tuning… Expand
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