基于稀疏正则化的卷积神经网络模型剪枝方法

现有卷积神经网络模型剪枝方法仅依靠自身参数信息难以准确评估参数重要性，容易造成参数误剪且影响网络模型整体性能。提出一种改进的卷积神经网络模型剪枝方法，通过对卷积神经网络模型进行稀疏正则化训练，得到参数较稀疏的深度卷积神经网络模型，并结合卷积层和BN层的稀疏性进行结构化剪枝去除冗余滤波器。在CIFAR-10、CIFAR-100和SVHN数据集上的实验结果表明，该方法能有效压缩网络模型规模并降低计算复杂度，尤其在SVHN数据集上，压缩后的VGG-16网络模型在参数量和浮点运算量分别减少97.3%和91.2%的情况下，图像分类准确率仅损失了0.57个百分点。

Pruning Method for Convolutional Neural Network Models Based on Sparse Regularization

The existing pruning algorithms for Convolutional Neural Network(CNN) models exhibit a low accuracy in evaluating the importance of parameters by relying on their own parameter information, which would easily lead to mispruning and affect the performance of model.To address the problem, an improved pruning method for CNN models is proposed.By training the model with sparse regularization, a deep convolutional neural network model with sparse parameters is obtained.Structural pruning is performed by combining the sparsity of the convolution layer and the BN layer to remove redundant filters.Experimental results on CIFAR-10, CIFAR-100 and SVHN datasets show that the proposed pruning method can effectively compress the network model scale and reduce the computational complexity.Especially on the SVHN dataset, the compressed VGG-16 network model reduces the amount of parameters and FLOPs by 97.3% and 91.2%, respectively, and the accuracy of image classification only loses 0.57 percentage points.