Can I read Pruning Feature Maps for Efficient Convolutional Neural Networks on EtoBox?

Pruning Feature Maps for Efficient Convolutional Neural Networks by Xiao-ting Guo; Xin-shu Xie; Xun Lang is a Engineering article available to read on EtoBox.

What is Pruning Feature Maps for Efficient Convolutional Neural Networks about?

In recent years, convolutional neural networks have become increasingly important in the field of machine learning, especially for computer vision. However, deep network models are difficult to deploy on hardware-constrained devices because of huge number of parameters, storage requirements and computational cost. This paper proposes a pruning feature maps method to delete redundant feature information in the deep network. Thus, it can simplify network structure at the same time reduce the computational complexity and speed up the operation. We first define a small chi-square supervised set. The feature maps of this set and the training set are extracted. Then, two variance matrices are constructed. The differences between the variance matrices are then used to establish chi-square distances. Through continuous experiments, the optimal position threshold is set, and the feature maps corresponding to the channels below the position threshold are pruned. Experiment performances show that the method can reduce network redundancy, storage space and network complexity totally up to 70%. This is accomplished without appreciably diminishing the network’s accuracy. In the worst case, the a

Who reads Pruning Feature Maps for Efficient Convolutional Neural Networks?

Author

Xiao-ting Guo; Xin-shu Xie; Xun Lang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)