## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.2 Ablation Experiments
At the same time, perceptual loss and edge loss are added to enable the model to learn the style characteristics and modeling of landscape paintings. In summary, the proposed Paint-CUT can generate landscape paintings with clear outlines, rich details, and realistic style, which improves the quality of generated landscape paintings. (2) Quantitative analysis  
In order to further verify the effects of shuffle attention (SA), perceptual loss, and edge loss on generating landscape paintings, the generated results of various models in Figures 10-12 are compared by calculating the IS and FID. And the comparison results are shown in Table 5, Table 6, and Table 7, respectively. A higher IS indicates that the generated results have higher quality and diversity. A lower FID indicates that the generated results have better quality. As shown in Table 5, the baseline CUT has the lowest IS and highest FID on the samples of Dwelling in the Fuchun Mountains, which indicates that the CUT cannot generate ideal landscape paintings. Qualitative analysis shown in Figure 10 suggests that the generated landscape paintings of CUT have unclear details and blurred outlines and the variation of ink is not obvious. SA can capture the detailed features of the main regions in photos. In order to highlight the details of the scenery, SA is added to the generator to construct the SA-ResBlock. The IS increases and the FID decreases, which indicates that the addition of SA is effective in generating landscape paintings. The perceptual loss (L\({}_{\text{per}}\)) can better constrain the content and style information of the generated landscape paintings. In order to make the generated landscape paintings highly consistent with the content of input landscape photos, and have the variation of ink, the perceptual loss (L\({}_{\text{per}}\)) is added on the basis of SA. After adding L\({}_{\text{per}}\), the IS increases while the FID decreases, which indicates that the addition of L\({}_{\text{per}}\) improves the results of landscape paintings. Lines can guide the generation of landscape paintings and rich line information can generate landscape paintings with richer detailed features. The proposed Paint-CUT is finally constructed after adding the edge loss (L\({}_{\text{edge}}\)). And the generated landscape paintings have the highest IS and lowest FID, which is consistent with the qualitative analysis. The experimental results show that the proposed Paint-CUT has a stronger feature learning ability and better-generated results, which can largely retain the content of landscape photos and, at the same time, learn the style of target landscape paintings. Similarly, the evaluation metrics of the ablation experiments on samples of A Thousand Li of Rivers and Mountains and the ChipPhi dataset are shown in Tables 6 and 7, respectively. It can be seen that after adding the SA to construct the SA-ResBlock, the model better captures the internal structural features and effectively retains the texture details of the landscape photos. At the same time, the perceptual loss (L\({}_{\text{per}}\)) and edge loss (L\({}_{\text{edge}}\)) are added to construct the proposed Paint-CUT, which can learn the style characteristics and modeling of landscape paintings. The generated results retain the content of landscape photos and have a similar style to the target landscape paintings, which improves the generation quality. The results of our model have the highest IS and lowest FID compared to other models, which is consistent with the qualitative analysis. In conclusion, the constructed Paint-CUT can generate landscape paintings with clear outlines, rich details, and a similar style to target landscape paintings, which improves the quality of generated landscape paintings.