To extract more expressive and discriminative key features,reduce the loss of key features during network transmission,and improve the image classification ability of neural networks,a new image classification network of gating mechanism (GMNet) is proposed. Firstly,the shallow features are extracted using gated convolution,and the convolution operation is selectively performed through the gating mechanism to improve the network's ability to extract key features of the original image. Secondly,an interpolation gated convolution (IGC) module is designed,which combines Lanczos inter-polation with gated convolution to enhance shallow features while extracting more discriminative features,improving the non-linear expression ability of features. Then,a large kernel gated attention mechanism (LGAM) module is designed,which combines large kernel attention with gated convolution to achieve selective enhancement and fusion of features,and improve the contribution of key region features. Finally,the large kernel gated attention mechanism module is embedded in-to the residual branch to enable the model to learn input data's features and contextual information more effectively,reduce the loss of key features during network information transmission,and improve the network's classification ability. The method achieved classification accuracy of 97.05％,83.68％,97.68％,90.60％,and 83.05％ on image datasets CIFAR-10,CIFAR-100,SVHN,Imagenette,and Imagewoof,respectively,and improved on average by 3.26％,7.08％,3.44％,2.65％,and 5.02％ compared to current advanced methods. Compared with existing mainstream network models,the gated mecha-nism image classification network proposed in this paper can enhance the non-linear expression ability of features,extract more expressive and discriminative vital features,the loss of key features,improve the contribution of key region features,and effectively improve the image classification ability of neural networks.
万方数据
维普
