74 citations · 112 across the 19 of their papers we have counts for
6 papers · 1 filter
GHFP: Gradually Hard Filter Pruning
Linhang Cai, Zhulin An, Yongjun Xu
Filter pruning is widely used to reduce the computation of deep learning, enabling the deployment of Deep Neural Networks (DNNs) in resource-limited devices. Conventional Hard Filt…
Channel Pruning via Multi-Criteria based on Weight Dependency
Yangchun Yan, Rongzuo Guo, Chao Li +2
Channel pruning has demonstrated its effectiveness in compressing ConvNets. In many related arts, the importance of an output feature map is only determined by its associated filte…
Softer Pruning, Incremental Regularization
Linhang Cai, Zhulin An, Chuanguang Yang +1
Network pruning is widely used to compress Deep Neural Networks (DNNs). The Soft Filter Pruning (SFP) method zeroizes the pruned filters during training while updating them in the…
Multi-view Contrastive Learning for Online Knowledge Distillation
Chuanguang Yang, Zhulin An, Yongjun Xu
Previous Online Knowledge Distillation (OKD) often carries out mutually exchanging probability distributions, but neglects the useful representational knowledge. We therefore propo…
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task
Chuanguang Yang, Zhulin An, Xiaolong Hu +2
Deep convolutional neural networks (CNN) always depend on wider receptive field (RF) and more complex non-linearity to achieve state-of-the-art performance, while suffering the inc…
Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants
Hui Zhu, Zhulin An, Kaiqiang Xu +2
Existing approaches to improve the performances of convolutional neural networks by optimizing the local architectures or deepening the networks tend to increase the size of models…