Self-Adaptive Network Pruning

By Jinting Chen, Zhaocheng Zhu, Chengwei Li, Yuming Zhao. 2019

24 citations recorded by the source corpus.

Deep convolutional neural networks have been proved successful on a wide range of tasks, yet they are still hindered by their large computation cost in many industrial scenarios. In this paper, we propose to reduce such cost for CNNs through a self-adaptive network pruning method (SANP). Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel. Given a total computation budget, SANP adaptively determines the pruning strategy with respect to each layer and each sample, such that the average computation cost meets the budget. This design allows SANP to be more efficient in computation, as well as more robust to datasets and backbones. Extensive experiments on 2 datasets and 3 backbones show that SANP surpasses state-of-the-art methods in both classification accuracy and pruning rate.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang
  7. Quoc V. Le
  8. Alan L. Yuille

In Computer Vision and Pattern Recognition

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Yizeng Han
  5. Le Yang
  6. Honghui Wang
  7. Luc Van Gool
  8. Alan L. Yuille

In Efficiency & Compression

  1. Shiji Song
  2. Gao Huang
  3. Yulin Wang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang

Citations in the corpus