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Automatic filter pruning algorithm for image classification
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DOI：10.1007/s10489-023-05207-x.png)
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Abstract
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Network pruning is an essential technique for compressing and accelerating convolutional neural networks (CNNs). Existing pruning algorithms primarily evaluate filter importance or similarity, and then remove unimportant filters or keep only one similar filter at each convolutional layer based on a global pruning ratio. These methods, ignoring the sensitivity of pruning among different convolutional layers, rely on a lot of manual experience and multiple experiments to obtain the optimal convolutional neural network structure. To this end, we propose an automatic filter pruning algorithm via feature map average similarity and reverse search genetic algorithm(RSGA), dubbed as AFPruner, which automatically searches for the optimal combination of pruning ratio for all convolutional layers, evaluates filter similarity by feature map average similarity and then prunes similarity filter. Our method is evaluated against several state-of-the-art CNNs on three different classification datasets, and the experimental results show that our algorithm outperforms most current network pruning algorithms.
Keywords:
Model compression
Convolutional neural network
Network pruning
Genetic algorithm
Feature map average similarity
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