## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.1 Comparison Experiments
In conclusion, the proposed Paint-CUT solves the problem of detail loss, poor style transfer, and blurred outlines in generated landscape paintings and can generate landscape paintings with the style of A Thousand Li of Rivers and Mountains from photos. The comparison results in Figure 8 show that our model generates better results. The generated landscape paintings are more similar to the original photos in content and target paintings in style. Finally, experiments are conducted on the samples of the ChipPhi dataset to demonstrate that the proposed Paint-CUT has good generalization ability. In total, 1000 landscape photos and 1000 landscape paintings are selected as training samples from the ChipPhi dataset. The learning rate is set to 0.0001. Another 100 photo samples are selected for testing. The comparison experiment results of generated landscape paintings are shown in Figure 9. The scene in the landscape paintings of the ChipPhi dataset is described with an  
Figure 8: Comparison results of generating landscape paintings with the style of A Thousand Li of Rivers and Mountains (Red boxes are used for comparison of details). (**a**) Landscape photo, (**b**) MUNIT, (**c**) NICE-GAN, (**d**) U-GAT-IT, (**e**) CycleGAN, (**f**) CUT, and (**g**) Ours. ink brush. As shown in Figure 9b, the generated results of MUNIT are similar to the ink wash style of ChipPhi. However, the content of the landscape paintings is deformed and is different from the landscape photos. This suggests that MUNIT cannot generate good results. As shown in Figure 9c, the generated results of NICE-GAN basically acquire the ink wash style of ChipPhi; however, the detailed information is not recovered well. Part of the scenery is generated incorrectly due to inconsistent recognition. The generated results of U-GAT-IT are more similar to the ink wash style of ChipPhi, as shown in Figure 9d. However, the outline of generated landscape paintings is unclear, and some areas are generated incorrectly. The generated results of CycleGAN are shown in Figure 9e; it can be seen that the results of CycleGAN are consistent with the content of the photos and, basically, have an ink wash style. However, the results still lose details (i.e., the mountains in the fourth row). As shown in Figure 9f, the generated results of CUT are similar to the input photos in terms of content and have basic scene information. But there are still some problems, such as simple texture, blurred outlines (i.e., distant mountains), and inconsistent overall color. Figure 9g shows the generated results of the proposed Paint-CUT; it can be seen that Paint-CUT can generate landscape paintings with the style of the ChipPhi dataset from photos. From the visual results, the generated landscape paintings of ours are similar to the original photos in terms of the content, which maintains the layout and content of the original photos. Therefore, the model leads to better-generated results. In conclusion, the proposed Paint-CUT solves the problem of detail loss, poor style transfer, and blurred outlines in generated landscape paintings and can generate landscape paintings with the style of the ChipPhi dataset from photos. The comparison results in Figure 9 show that our model generates better results both in content and style. In a word, the analysis of generated results shows that the proposed Paint-CUT, based on the constructed dataset and the ChipPhi dataset, solves the problems of detail  
Figure 9: Comparison results of generating landscape paintings with the style of the ChipPhi dataset (Red boxes are used for comparison of details). (**a**) Landscape photo, (**b**) MUNIT, (**c**) NICE-GAN, (**d**) U-GAT-IT, (**e**) CycleGAN, (**f**) CUT, and (**g**) Ours. loss, poor style transfer, and blurred outlines in the present generated results. The SA is used to construct the SA-ResBlock, which enables the model to better capture the internal structural features and effectively retain the texture details of the photos.