ECPANet:An attention-based method for channel pruning of deep convolutional neural networks
In the field of deep learning,the rapid development of Convolutional Neural Networks(CNNs)has led to advanced models that require substantial computational and storage resources.However,deploying these models on resource-constrained and highly real-time embedded devices has become increasingly challenging.To address this issue,channel pruning has become one of the primary methods for network compression.Traditional channel pruning methods suffer from accuracy degradation and difficul-ties in determining channel importance.To tackle these issues,an efficient channel attention pruning method has been proposed.This method involves embedding ECPANet modules into deep convolutional neural networks to enhance their representational ca-pacity,evaluate the importance of each channel in feature maps,and prune unimportant channels based on their importance factors to reduce the model's size and computational load.Experimental results show that,compared to traditional channel pruning meth-ods,attention-based channel pruning methods can more accurately determine channel importance,thereby improving pruning effec-tiveness and model performance.
deep convolutional neural networkchannel pruningattention mechanism
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