.png){kind=icon}
Return
A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2022.3164328.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
ThiNet is a recent method for pruning convolutional neural networks. This method uses a norm of a subset of the components of the output resulting from the convolutional layer succeeding the layer from which the filters are to be removed for pruning the network. The ThiNet algorithm is very time-consuming, in view of the fact that the filters for removal are selected one by one iteratively. In this paper, we propose a modified version of ThiNet, in which the same information on the output of the same convolutional layer as used by ThiNet is employed to select all the filters together in a single step, for pruning the network. The proposed modified algorithm is shown to have a time-complexity that is only a small fraction of that of ThiNet or any other state-of-the-art algorithm and that the pruned network has almost the same reduction in its accuracy as that of the network pruned by ThiNet.
Keywords:
Signal processing algorithms
Convolution
Convolutional neural networks
Training
Testing
Tensors
Mathematical models
CNN model compression
convolutional neural networks
network pruning
ThiNet algorithm
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
Calcium-modulating cyclophilin ligand regulates membrane trafficking of postsynaptic GABAA receptors
OAAI
.png){kind=icon}
OAAIShareSaveModel Compression and Acceleration for Deep Neural Networks The principles, progress, and challenges
PREAI
ShareSaveShareSaveShareSave
View more