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Adaptation Art Image Style Transfer by Integrating CSDA-FD Algorithm and OSDA-DS Algorithm

Wang Peng

Machine Graphics and Vision

2025

Vol. 34, No. 4

43--63

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 im-ages 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, F1score, 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.