## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.1 Comparison Experiments
From the visual results, the generated landscape paintings of ours are similar to the original photos in terms of content, such as the outline of mountains and the texture of rocks. And the results have rich details and the realistic style of Dwelling in the Fuchun Mountains. In conclusion, the proposed Paint-CUT solves the problem of detail loss, poor style transfer, and blurred outlines in present generated results and can generate landscape paintings with the style of Dwelling in the Fuchun Mountains from photos. The comparison results in Figure 7 show that our model generates better results compared with others. The generated landscape paintings not only maintain the layout and content of the target photos but also reflect the characteristics of Dwelling in the Fuchun Mountains with ink wash style, which has better-generated results. Secondly, experiments are conducted on the samples of A Thousand Li of Rivers and Mountains in the constructed dataset, which generates landscape paintings with the style of A Thousand Li of Rivers and Mountains from landscape photos. A total of 500 landscape photos and 500 landscape paintings with the style of A Thousand Li of Rivers and Mountains are selected as training samples from the constructed dataset. The learning rate is set to 0.00015. Another 100 photo samples are selected for testing. The comparison experiment results of generated landscape paintings are shown in Figure 8. The samples of A Thousand Li of Rivers and Mountains are meticulous with the style of blue and green, which describes the beauty of the southern scenery. The generated results of MUNIT are shown in Figure 8b. The content of the paintings is quite different from the landscape photos and fails to recover the main scenery in the picture. At the same time, the color is also different from the style of blue and green. Since A Thousand Li of Rivers and Mountains emphasizes delicate brush strokes and the style of blue and green, MUNIT fails to generate satisfactory results. As shown in Figure 8c, the generated results of NICE-GAN have more obvious style features than MUNIT; however, the generated landscape paintings miss some elements. The background color of the first row in Figure 8c is incorrect and it cannot generate landscape paintings with the style of blue and green well. Figure 7: Comparison results of generating landscape paintings with the style of Dwelling in the Fuchun 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. As shown in Figure 8d, some generated results of U-GAT-IT miss a large range of content. For example, the generated scenery in the third row of Figure 8d cannot be distinguished. The generated result loses essential structural information and local details also cannot be generated. As shown in Figure 8e, the generated results of CycleGAN are similar to the input photos in terms of content. However, the details and lines are blurred and the distribution of color is also unreasonable. The generated results of CUT are better than the above models, which are shown in Figure 8f. However, the details are not clear enough and the style is not apparent. The generated results of the proposed Paint-CUT are shown in Figure 8g. It can be seen that the proposed Paint-CUT can generate landscape paintings with the style of A Thousand Li of Rivers and Mountains from photos. From the visual results, the generated landscape paintings of ours are similar to the original photos in terms of content. As shown in the second row in Figure 8g, the details of the trees in the original photo are maintained in the generated result. And the generated landscape paintings not only maintain the layout and content of the target photos but also reflect the style characteristics of blue and green, which have better-generated results.