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Traditional domain adaptation learning methods have a strong dependence on data labels. The transfer process can easily lead to a decrease in training set performance, affecting the effectiveness of transfer learning. Therefore, this study proposes a domain adaptation model that combines feature disentangling and disentangling subspaces. The model separates the content and style features of images through disentangling, effectively improving the quality of image transfer. From the results, the proposed feature disentangling algorithm achieved pixel accuracy of over 84% for semantic segmentation of 14 categories, including roads, sidewalks, and buildings, with an average pixel accuracy of 85.2%. On the ImageNet, the precision, recall, F₁ score, and overall accuracy of the research algorithm were 0.942, 0.898, 0.854, and 0.841, respectively. Compared with the One-Class Support Vector Machine, the precision, recall, F₁, and overall accuracy were improved by 8.4%, 10.3%, 27.8%, and 10.9%, respectively. The proposed model can accurately recognize and classify images, providing effective technical support for image transfer.
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