## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.2 Ablation Experiments
SA can capture detailed features of landscape photos. As shown in the red box in Figure 11c, after adding the SA, the mountain trend becomes clearer and the texture details become richer. However, some details are still lost. The style of blue and green becomes clearer, but the colors of various scenes are still not accurate enough. The perceptual loss (L\({}_{\text{per}}\)) can better constrain the content and style information of generated landscape paintings. As shown in the red box in Figure 11d, after adding the perceptual loss (L\({}_{\text{per}}\)), the lost brush strokes in the mountains are recovered and the color distribution is more reasonable. However, the outlines and texture details of mountains are still unclear. The edge loss (L\({}_{\text{edge}}\)) can constrain the line information of landscape paintings and the generated results have clearer outlines and richer details. The proposed Paint-CUT is finally constructed after adding the edge loss (L\({}_{\text{edge}}\)). As shown in the red box in Figure 11e, the outlines of the mountains become clear and the texture of mountains is generated. In conclusion, based on the proposed Paint-CUT, the ablation experiments in Figure 11 prove that the generated landscape paintings retain the content of photos and have a similar style to A Thousand Li of Rivers and Mountains, which improves the generation quality. In addition, ablation experiments on the ChipPhi dataset are conducted to demonstrate that the SA, perceptual loss, and edge loss in the proposed Paint-CUT are equally necessary for generating landscape paintings on different datasets. The ablation experiment results on the ChipPhi dataset are shown in Figure 12. The generated landscape painting of CUT in Figure 12b is similar to the content of the input landscape photo. But, as shown in the red box, the generated result has some missing parts in mountains and the overall color of the painting is weak. The SA can capture detailed features of the main regions  
Figure 11: Generating results of ablation experiments on samples of A Thousand Li of Rivers and Mountains. (**a**) Landscape photo, (**b**) CUT, (**c**) CUT + SA, (**d**) CUT + SA + L\({}_{\text{per}}\), and (**e**) CUT + SA + L\({}_{\text{per}}\) + L\({}_{\text{edge}}\) (Paint-CUT). Figure 10: Generating results of ablation experiments on samples of Dwelling in the Fuchun Mountains. (**a**) Landscape photo, (**b**) CUT, (**c**) CUT + SA, (**d**) CUT + SA + L\({}_{\text{per}}\), and (**e**) CUT + SA + L\({}_{\text{per}}\) + L\({}_{\text{edge}}\) (Paint-CUT). in photos. As shown in the red box in Figure 12c, after adding the SA, the vacancy of mountains is generated and the texture details are richer; however, there are still some details missing. The perceptual loss (\(\rm{L_{per}}\)) can better constrain the content and style information of the generated landscape paintings. As shown in the red box in Figure 12d, after adding the perceptual loss (\(\rm{L_{per}}\)), the missing part in the mountains is recovered and the generated landscape painting has richer details. However, some textures and lines are still missing. The edge loss (\(\rm{L_{edge}}\)) can constrain the line information of landscape paintings; the generated results have clearer outlines and richer details. The proposed Paint-CUT is finally constructed after adding the edge loss (\(\rm{L_{edge}}\)). As shown in the red box in Figure 12e, the outlines of mountains become clear and the whole painting is highly similar to the content of the input landscape photo. In a word, based on the proposed Paint-CUT, the ablation experiments in Figure 12 prove that the generated landscape paintings retain the content of photos and have a similar style to the target landscape paintings, which improves the generation quality. In conclusion, the proposed Paint-CUT uses shuffle attention (SA) to construct the SA-ResBlock, which enables the model to better capture the internal structural features and effectively retain the texture details of the landscape photos.