改进的轻量型网络在图像识别上的应用

Improved Lightweight Network in Image Recognition

针对卷积神经网络在图像识别任务上模型复杂度大、参数量多,首先提出了一种轻量化的SepNet网络结构,该结构在分类器模块上采用克罗内克积替换了传统的全连接层。为进一步优化网络结构,在特征提取模块均衡网络深度、宽度,设计了一个利用深度可分离卷积和残差网络的可分离残差模块,最终形成了一个能实现端到端训练的轻量化网络架构,称为sep_res18_s3。实验分别在MNIST、CIFAR-10、CIFAR-100数据集上验证SepNet的有效性,设计的SepNet网络结构相比VGG10,参数数量和运算量在不损失其精度下均降低了94.15%。同时,相比设计的类残差网络cov_res18_s3,sep_res18_s3仍能降低58.33%的参数量和81.82%的FLOPs。实验结果表明,采用克罗内克积替换全连接层可以在保证训练结果准确度的同时显著降低参数数量和计算成本,并在一定程度上防止过拟合,在此基础上结合深度可分离卷积和类残差结构,证明了sep_res18_s3的有效性。

To solve the complexity of convolutional neural network and the large number of parameters in image recognition task,this paper proposes a lightweight network SepNet.In this structure,the traditional fully-connected layer is replaced by Kronecker product in the classifier module.In order to further optimize network structure,in the feature extraction module,by balancing the depth and width of the network,a separable residual network module using the deep separable convolution and residual network is designed.Finally,a lightweight network architecture which can realize end-to-end training is formed,which is called sep_res18_s3.The experiments are conducted on MNIST,CIFAR-10 and CIFAR-100 datasets respectively.The results show that compared with the VGG10 network,the designed SepNet can reduce the number of parameters and computation by 94.15%without losing its accuracy.At the same time,compared with cov_res18_s3,sep_res18_s3 can still reduce the parameter amount by 58.33%and 81.82%of FLOPs.Experimental results show that replacing the fully-connected layer with Kronecker product can not only maintain the accuracy of training results,but also significantly reduce the number of parameters and calculation costs,and to a certain extent,it can prevent overfitting.On this basis,combining the deep separable convolution and residual structure,it proves the effectiveness of sep_res18_s3.