102 citations · 194 across the 9 of their papers we have counts for
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cs.LG2021★ 7 cited
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
Yikai Zhang, Hui Qu, Qi Chang +3
Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple…
cs.LG2020★ 64 cited
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao, Ting Liu, Xi Peng +1
Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions. However, it is difficult to define heuristic…