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Federated Generative Adversarial Learning
[article]
2020
arXiv
pre-print
This work studies training generative adversarial networks under the federated learning setting. Generative adversarial networks (GANs) have achieved advancement in various real-world applications, such as image editing, style transfer, scene generations, etc. However, like other deep learning models, GANs are also suffering from data limitation problems in real cases. To boost the performance of GANs in target tasks, collecting images as many as possible from different sources becomes not only
arXiv:2005.03793v3
fatcat:bkrsva6iubfdxgzq43blalx74m