most citedDRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2020

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…

cs.CV2020

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…

cs.CV20191 cited

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…

cs.CV2019

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…

cs.CV2019

Gated Convolutional Networks with Hybrid Connectivity for Image Classification

Chuanguang Yang, Zhulin An, Hui Zhu +5

We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our…

cs.NE2019

EENA: Efficient Evolution of Neural Architecture

Hui Zhu, Zhulin An, Chuanguang Yang +3

Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every inter…