Abstract：Multi-representation adaptive network（MRAN） has achieved remarkable results in unsupervised learning.However，the feature extraction of MRAN only pays attention to the relationship between the domain in the spatial structure and ignores the relationship between the feature channels.When performing unsupervised domain adaptation（UDA） classification，there is a large amount of confusing data near the decision boundary.When using information entropy minimization to classify confusing data，misclassification often occurs.To solve this problem，a Multi-Representation Squeeze-Excitation Adaptation Network_Batch Kernel Norm Maximization（MRSEAN_BNM） is proposed.The network uses the squeeze incentive attention mechanism to re-calibrate multiple characterization features to strengthen important characterization features.It uses Conditional Maximum Mean Difference（CMMD） to narrow the feature distribution distance between the source domain and the target domain，and maximizes the target domain The kernel norm of the classification output matrix is used to constrain the confusing data of the decision boundary，so as to achieve the effect of improving the accuracy of the domain to adapt to the image classification.The experimental results of image classification and visualization results based on the domain adaptation of the public data set show that the classification accuracy of MRSEAN_BNM has been significantly improved.
2022, Vol. 43 
