122 citations · 156 across the 7 of their papers we have counts for
5 papers · 1 filter
Greedy Network Enlarging
Chuanjian Liu, Kai Han, An Xiao +4
Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracy, such as Effici…
Augmented Shortcuts for Vision Transformers
Yehui Tang, Kai Han, Chang Xu +4
Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation a…
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…
Manifold Regularized Dynamic Network Pruning
Yehui Tang, Yunhe Wang, Yixing Xu +4
Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared wit…
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Ying Nie, Kai Han, Zhenhua Liu +2
Modern single image super-resolution (SISR) system based on convolutional neural networks (CNNs) achieves fancy performance while requires huge computational costs. The problem on…