## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.2 Ablation Experiments  
Chinese landscape painting focuses on artistic conception and lines, which mainly describe the natural landscape of mountains and rivers. In order to evaluate the resulting improvement by shuffle attention (SA), perceptual loss, and edge loss, ablation experiments are conducted on the proposed Paint-CUT. The SA, perceptual loss, and edge loss are sequentially added to the baseline CUT to study improvements in detail, style, and outlines. The ablation experiment results on the constructed dataset and ChipPhi dataset will be analyzed in detail in this section. Qualitative analysis  
The ablation experiment results on the constructed dataset of Dwelling in the Fuchun Mountains are shown in Figure 10. The baseline CUT generates landscape paintings with the basic style of Dwelling in the Fuchun Mountains in Figure 10b; however, the details of the scenery are unclear. For example, the houses and outlines of mountains in the red box are blurred and the ink wash style is not obvious. The generated result is unsatisfactory. Shuffle attention (SA) can capture the detailed features of the main regions in photos. In order to solve the problem of detail loss and focus on the main scenery in landscape painting, SA is added to the generator to construct the SA-ResBlock. As shown in Figure 10c, the generated landscape painting has more detailed information than the result of the baseline model, i.e., the texture of mountains has a variation of ink wash and the details of houses are richer. However, these details are still blurred and cannot change according to the scenery characteristics. The perceptual loss (L\({}_{\text{per}}\)) can better constrain the content and style information of generated landscape paintings. In order to solve the problem of unclear style, we continue to add perceptual loss (L\({}_{\text{per}}\)) so that the generated landscape paintings are highly consistent with the content of the input photos and the details of scene information become richer. At the same time, the generated results have variations of ink wash. As shown in Figure 10d, the ink of the mountains and houses is consistent with the light and dark areas of photos. In addition, lines can guide the generation of landscape paintings. More outline information can be obtained based on the lines of scenery, such as the trend of mountains and waves of water. Meanwhile, rich line information can generate landscape paintings with more detailed features. In order to further improve the quality of landscape paintings and solve the problem of blurred outlines and details, we continue to add edge loss (L\({}_{\text{edge}}\)) and then construct the proposed Paint-CUT. As the edge loss (L\({}_{\text{edge}}\)) can constrain the line information of landscape paintings, the generated results have clearer outlines and richer details. As shown in the red box in Figure 10e, the details of mountains are clearer and the doors and windows of houses are visible. In conclusion, compared with the baseline model, the ablation experiments in Figure 10 prove that the proposed Paint-CUT improves the generation quality of landscape paintings. Specifically, the SA is used to construct the SA-ResBlock, which enables the model to better capture the internal structural features of the landscape photos and effectively retain the texture details. At the same time, the perceptual loss and edge loss are added so that the model can learn the style characteristics and modeling of landscape paintings. Finally, the generated landscape paintings retain the content of photos and have the similar style to Dwelling in the Fuchun Mountains. The ablation experiment results on the constructed dataset of A Thousand Li of Rivers and Mountains are shown in Figure 11. The landscape painting generated by the baseline CUT in Figure 11b basically possesses the style of blue and green; however, as shown in the red box, the outlines of the generated landscape painting are not clear and a lot of basic brush strokes are lost while the colors are also vague.