Name
Title | ||
|---|---|---|
Channel pruning based on mean gradient for accelerating Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
•Channel pruning is applied to reducing huge memory consumption and high computational complexity of convolutional neural networks.•New pruning criterion based on mean gradient does well in measure the importance of channels in network performance.•Hierarchical global pruning strategy, which improves global pruning strategy, achieves significant reduction in Float Point Operations of networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.sigpro.2018.10.019 | Signal Processing |
Keywords | Field | DocType |
Channel pruning,Convolutional Neural Networks,Mean gradient,Hierarchical global pruning,Acceleration | Mathematical optimization,FLOPS,Convolutional neural network,Floating point,Algorithm,Communication channel,Mathematics,Pruning,Computational complexity theory,Network performance | Journal |
Volume | ISSN | Citations |
156 | 0165-1684 | 3 |
PageRank | References | Authors |
0.42 | 21 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congcong Liu | 1 | 3 | 1.43 |
| Huaming Wu | 2 | 81 | 14.49 |