Abstract: Pooling operation is an essential part of deep convolutional neural networks,and also one of the key factors for the success of deep convolutional neural network.However,in the process of image recognition,the traditional direct pooling operation will lead to the loss of feature information and affect the accuracy of recognition.In this paper,a dual-field feature fusion module based on discrete wavelet transform is proposed to overcome the disadvantage of the direct pooling operation.In this module,the dual-field feature fusion of spatial domain and channel domain is considered,and the pooling operation is embedded between spatial feature fusion module and channel feature fusion module,which effectively suppress the information loss of features caused by pooling directly.By replacing the existing pooling operation,the new dual-field feature fusion module can be easily embedded into the current popular deep neural network architectures.Extensive experimental results on CIFAR-10,CIFAR-100 and Mini-Imagenet datasets by using mainstream network architectures such as VGG,ResNet and DenseNet.The experimental results show that compared with the classical network,the popular network based on embedded attention mechanism or latest wavelet basis model,the proposed method can achieve higher classification accuracy.

Key words: Attention mechanisms, Deep convolutional neural networks, Discrete wavelet transform, Feature fusion, Pooling operation