A Review of Network Compression based on Deep Network Pruning
Jie Yu, Sheng Tian
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.53How to use a DOI?
- Network pruning, Deep learning, Convolutional neural network.
- In recent years, the deep network has made considerable achievements in the field of computer vision and gradually becomes a hot research topic. The performance of the deep network is very good, however, due to its large size of parameters, high storage, and computational cost, it is hard to deploy the deep network on limited hardware platforms (such as mobile devices). The parameters of the model can express its complexity to some extent, but related studies have shown that not all parameters work in the model. Some parameters are useless, redundancy, and even degrade the performance of the model. Firstly, this paper sorts the results achieved by the scholars domestic and overseas in the field of deep network pruning, and sums up the pruning methods based on single weight granularity, kernel weight granularity and channel granularity; Then, summarizes the effect of the relevant pruning methods on a variety of public deep network models; Finally, it combs the achievements of the current researches and thoughts of network pruning, summarizes the important progress and discusses the future directions.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jie Yu AU - Sheng Tian PY - 2019/04 DA - 2019/04 TI - A Review of Network Compression based on Deep Network Pruning PB - Atlantis Press SP - 308 EP - 319 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.53 DO - https://doi.org/10.2991/icmeit-19.2019.53 ID - Yu2019/04 ER -