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ReLP: Reinforcement Learning Pruning Method Based on Prior Knowledge

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Abstract

Filter pruning is one of the main methods of neural network model compression. Existing filter pruning methods rely more on experience based on manual techniques and are less efficient, while local optimal solutions are prone to appear based on greedy or heuristic algorithms. Some researchers use reinforcement learning to determine the compression strategy automatically but lack the guidance of network characteristics and prior knowledge, and the pruning efficiency needs to be improved. To this end, we propose a reinforcement learning pruning method based on prior knowledge to address these issues. Firstly, we rank the filters globally and obtain the position variables α, k, and the rank scaling factor r. Then, the relevant variables of the filter are passed into the deep deterministic policy gradient agent as prior knowledge through the concept of defined importance filters. Finally, a reinforcement learning-based automated pruning method is used to iteratively perform filter selection and parameter optimization. We verify the effectiveness of this method through extensive experiments. The experiments use three mainstream neural network models, including VGG, ResNet, and MobileNet, to compare the performance of our method with others on the CIFAR-10/100 and ImageNet datasets. On the ImageNet dataset, when the accuracy of MobileNetV2 is only reduced by 0.82%, there are only 59.62% of the original FLOPs and 48.4% of the parameters.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Grant No.61976098), Science and Technology Development Foundation of Quanzhou City (Grant No.2020C067).

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Correspondence to Weiwei Zhang.

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Zhang, W., Ji, M., Yu, H. et al. ReLP: Reinforcement Learning Pruning Method Based on Prior Knowledge. Neural Process Lett 55, 4661–4678 (2023). https://doi.org/10.1007/s11063-022-11058-3

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