Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in recent years. Compressing these models not only reduces storage requirements, making deployment to edge devices feasible, but also accelerates inference, thereby reducing latency and computational costs. Structured pruning, which removes filters at the layer level, directly modifies the model architecture. This approach achieves a more compact architecture while maintaining target accuracy, ensuring that the compressed model retains good compatibility and hardware efficiency. Our method is based on a key observation: filters in different layers of a neural network have varying importance to the model's performance. When the number of filters to prune is fixed, the optimal pruning distribution across different layers is uneven to minimize performance loss. Layers that are more sensitive to pruning should account for a smaller proportion of the pruning distribution. To leverage this insight, we propose RL-Pruner, which uses reinforcement learning to learn the optimal pruning distribution. RL-Pruner can automatically extract dependencies between filters in the input model and perform pruning, without requiring model-specific pruning implementations. We conducted experiments on models such as GoogleNet, ResNet, and MobileNet, comparing our approach to other structured pruning methods to validate its effectiveness. Our code is available at https://github.com/Beryex/RLPruner-CNN.

本研究解决了卷积神经网络压缩与加速中的结构化剪枝问题，通过引入强化学习，自动学习最佳剪枝分布，从而在保持模型准确性的同时，实现更高效的模型压缩。实验结果表明，RL-Pruner在多种网络模型上表现优异，为神经网络的部署与应用提供了重要的价值。

RL-Pruner：基于强化学习的卷积神经网络结构化剪枝与加速