1k citations · 1.3k across the 26 of their papers we have counts for
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Federated Learning with Positive and Unlabeled Data
Xinyang Lin, Hanting Chen, Yixing Xu +4
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation…
On Positive-Unlabeled Classification in GAN
Tianyu Guo, Chang Xu, Jiajun Huang +4
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs.…
Data-Free Learning of Student Networks
Hanting Chen, Yunhe Wang, Chang Xu +6
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones…
Learning Student Networks via Feature Embedding
Hanting Chen, Yunhe Wang, Chang Xu +2
Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on…
Robust Student Network Learning
Tianyu Guo, Chang Xu, Shiyi He +3
Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as tea…
A Survey on Multi-view Learning
Chang Xu, Dacheng Tao, Chao Xu
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple…