1 citations · 1 across the 2 of their papers we have counts for
8 papers
TargetDrop: A Targeted Regularization Method for Convolutional Neural Networks
Hui Zhu, Xiaofang Zhao
Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped inf…
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…
DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners
Xiaolong Hu, Zhulin An, Chuanguang Yang +3
Convolutional neural networks (ConvNets) are widely used in real life. People usually use ConvNets which pre-trained on a fixed number of classes. However, for different applicatio…
Rethinking the Number of Channels for the Convolutional Neural Network
Hui Zhu, Zhulin An, Chuanguang Yang +3
Latest algorithms for automatic neural architecture search perform remarkable but few of them can effectively design the number of channels for convolutional neural networks and co…