## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.1 Comparison Experiments  
(1) Qualitative Analysis  
In order to verify the effectiveness of the proposed Paint-CUT in generating landscape paintings, MUNIT [25], NICE-GAN [26], U-GAT-IT [27], CycleGAN [28], and CUT [22] are selected to compare the generated results with the proposed Paint-CUT. MUNIT is a multimodal unsupervised image-to-image translation model, which samples from both a content space and style space and reconstructs them to generate final results. NICE-GAN contends a new role of the discriminator by reusing it to encode the images of the target domain. U-GAT-IT incorporates a new attention module and a learnable normalization parameter. The attention module guides the model to focus on important regions, distinguishing between source and target domains. In addition, AdaIN is introduced to control shape and texture changes. CycleGAN is a generative adversarial network for image translation with unpaired data, which consists of two generators and two discriminators in a ring network. In this paper, we compare the above models with the proposed Paint-CUT on the constructed dataset and ChipPhi dataset, respectively, and analyze their generated results. Firstly, experiments are conducted on the samples of Dwelling in the Fuchun Mountains in the constructed dataset, which generates landscape paintings with the style of Dwelling in the Fuchun Mountains from landscape photos. In total, 1000 landscape photos and 1000 landscape paintings with the style of Dwelling in the Fuchun Mountains are selected as training samples from the constructed 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 7. The samples of Dwelling in the Fuchun Mountains have elegant ink with an appropriate layout of mountains and water. As shown in Figure 7b, the generated results of MUNIT have a similar ink wash style to Dwelling in the Fuchun Mountains. However, the content is quite different from the input landscape photo, which only has a general outline and loses specific texture features. MUNIT samples from both a content space and style space and reconstructs them to obtain the final generated results. Due to the complex structural features and style features of landscape paintings, the generated results of MUNIT are not ideal. The generated paintings of NICE-GAN are shown in Figure 7c. Although there is some improvement in content and style compared with MUNIT, some generated results still have incomplete content and generated errors, such as a large blank area in the mountains of the fourth row in Figure 7c. The generated results of U-GAT-IT, which incorporate attention and maintain the basic structure and detailed information of the generated landscape paintings, are shown in Figure 7d. However, for some photos with indistinct boundaries in Figure 7d, the generated landscape paintings by U-GAT-IT have obvious missing parts and the details are not recovered well. The generated results of CycleGAN, which uses cycle consistency loss to ensure the accuracy of translation results, are shown in Figure 7e. The landscape paintings generated by CycleGAN are similar to the style of Dwelling in the Fuchun Mountains; however, some details are still missing in the landscape paintings. The generated results of CUT, which uses contrastive loss to replace cycle consistency loss and recovers the main content of the landscape paintings, are shown in Figure 7f. However, the detailed information is still missing and the style is not similar enough to the Dwelling in the Fuchun Mountains. The generated results of the proposed Paint-CUT are shown in Figure 7g. The SA is used to construct the SA-ResBlock in the Paint-CUT, which enables the model to better capture the internal structural features and effectively retain the texture details of the landscape photos. At the same time, perceptual loss and edge loss are added to learn the style features and modeling of landscape paintings.