Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Latest commit 89180a9 Aug 9, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
README.md Update README.md Aug 9, 2019

README.md

MetaPruning

This is the pytorch implementation of our paper "MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning", https://arxiv.org/abs/1903.10258, published in ICCV 2019.

Traditional pruning decides pruning which channel in each layer and pays human effort in setting the pruning ratio of each layer. MetaPruning can automatically search for the best pruning ratio of each layer (i.e., number of channels in each layer).

MetaPruning contains two steps:

  1. train a meta-net (PruningNet), to provide reliable weights for all the possible combinations of channel numbers in each layer (Pruned Net structures).
  2. search for the best Pruned Net by evolutional algorithm and evaluate one best Pruned Net via training it from scratch.

Citation

If you use the code in your research, please cite:

@article{liu2019metapruning,
  title={MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning},
  author={Liu, Zechun and Mu, Haoyuan and Zhang, Xiangyu and Guo, Zichao and Yang, Xin and Cheng, Tim Kwang-Ting and Sun, Jian},
  journal={arXiv preprint arXiv:1903.10258},
  year={2019}
}

Code and models

Available on https://github.com/liuzechun/MetaPruning

Contact

If you have any questions, please do not hesitate to contact Zechun Liu (zliubq@connect.ust.hk).

You can’t perform that action at this time.