Name
Title | ||
|---|---|---|
EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression |
Abstract | ||
|---|---|---|
Model compression methods have become popular in recent years, which aim to alleviate the heavy load of deep neural networks (DNNs) in real-world applications. However, most of the existing compression methods have two limitations: 1) they usually adopt a cumbersome process, including pretraining, training with a sparsity constraint, pruning/decomposition, and fine-tuning. Moreover, the last three... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNNLS.2020.3018177 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Matrix decomposition,Sparse matrices,Training,Neural networks,Automation,Optimization,Hardware | Journal | 32 |
Issue | ISSN | Citations |
10 | 2162-237X | 1 |
PageRank | References | Authors |
0.34 | 11 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofeng Ruan | 1 | 1 | 0.68 |
| Yufan Liu | 2 | 15 | 3.93 |
| Chunfeng Yuan | 3 | 418 | 30.84 |
| Bing Li | 4 | 217 | 60.28 |
| Weiming Hu | 5 | 5300 | 261.38 |
| Li Yangxi | 6 | 34 | 5.75 |
| Stephen J. Maybank | 7 | 4105 | 493.12 |