Research On Pruning Methods for Mobilenet Convolutional Neural Network

Abstract

This paper comprehensively reviews pruning methods for MobileNet convolutional neural networks. MobileNet is a lightweight convolutional neural network suitable for resource-constrained environments such as mobile devices.Various pruning methods can be applied to reduce the model's storage space and computational complexity, including channel pruning, kernel pruning, and weight pruning. Channel pruning removes unimportant channels to reduce redundant parameters and computations in the model, while kernel pruning reduces redundant calculations by pruning convolutional kernels. Weight pruning involves setting small-weighted elements to zero to remove unimportant weights. These pruning methods can be used individually or in combination. After pruning, fine-tuning is necessary to restore the model's performance. Factors such as pruning rate, pruning order, and pruning location need to be considered to achieve a balance between reducing model size and computational complexity while minimizing performance loss. Pruning methods based on MobileNet convolutional neural networks reduce the parameter count and computational complexity, improving model lightweightness and inference efficiency. These methods are of significant value in resource-constrained environments such as mobile devices. This review provides insights into pruning methods for MobileNet convolutional neural networks and their applications in lightweight and efficient model deployment. Further advancements such as automated pruning methods driven by reinforcement learning algorithms can enhance the pruning process to achieve optimal model compression effects. Future research should focus on adapting and optimizing these pruning methods for specific problem domains and achieving even higher compression ratios and computational speedups.