Lightweight CNN Design Based on Mixup Data Augmentation and Network Pruning

Convolutional Neural Networks (CNNs) have achieved remarkable success in image classification and other vision tasks in recent years. However, their large model size and computational complexity hinder their application in mobile terminals and embedded devices. To address this issue, this paper proposes a lightweight CNN design method that combines Mixup data augmentation and network pruning. The method aims to balance the trade-off between model compression and performance preservation, achieving the maximum model compression while maintaining as much of the original performance as possible. Using the FashionMNIST dataset as the experimental platform, a classification model based on a simplified LeNet structure is constructed. The model is evaluated under four different settings: the standard model, the Mixup-augmented model, the pruned sparse model, and the collaborative model integrating both Mixup augmentation and pruning. The experimental results show that Mixup enhances the model's generalization ability and robustness, pruning significantly reduces the number of parameters, and the combination of both achieves superior lightweight performance while preserving accuracy. This study demonstrates the effectiveness of Mixup and pruning techniques in collaborative optimization and proposes practical optimization strategies for deploying lightweight neural networks in resource-constrained environments.