NeurIPS 2020poster156 citations
Pruning Filter in Filter
Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun
Abstract
Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pruning (FP) and weight pruning (WP). FP wins at hardware compatibility but loses at the compression ratio compared with WP. To converge the strength of both methods, we propose to prune the filter in the filter. Specifically, we treat a filter F, whose size is C
BibTeX
@inproceedings{NEURIPS2020_ccb1d45f,
author = {Meng, Fanxu and Cheng, Hao and Li, Ke and Luo, Huixiang and Guo, Xiaowei and Lu, Guangming and Sun, Xing},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {17629--17640},
publisher = {Curran Associates, Inc.},
title = {Pruning Filter in Filter},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/ccb1d45fb76f7c5a0bf619f979c6cf36-Paper.pdf},
volume = {33},
year = {2020}
}