## 3 Experiment and Result Analysis
### Experimental Results
#### 3.3.1 Comparison Experiments
At the same time, perceptual loss and edge loss are added to learn the style features and modeling of landscape paintings. The generated landscape paintings have rich details (i.e., stone texture), clear outlines (i.e., outlines of distant mountains), and realistic style. In addition, the proposed Paint-CUT generates better landscape paintings both on the constructed dataset and the public ChipPhi dataset compared with other models, which indicates that our model has good generalizability. Quantitative analysis  
Inception score (IS) [29] and Frechet Inception Distance (FID) [30] are two evaluation metrics for generative models to measure the quality and diversity of generated results. IS calculates the KL-Divergence between the probability distribution of generated images and the real images. A high IS indicates that the generated results have higher quality and diversity. The IS is defined as follows:  
\[\text{IS}(G)=\exp\left(\mathbb{E}_{\text{x}\sim p_{g}}D_{KL}(p(\text{y}|\text{ x})\parallel p(\text{y}))\right) \tag{10}\]  
where \(\mathbb{E}\) is expectation, \(p_{g}\) is a distribution encoded by generative model \(G\), \(\text{x}\sim p_{g}\) indicates that x is an image sampled from \(p_{g}\), \(D_{KL}(p\parallel q)\) is the KL-Divergence between distributions \(p\) and \(q\), \(p(\text{y}|\text{x})\) is the conditional class distribution denoting the probability that image x belongs to class y, and \(p(\text{y})=\int_{x}p(\text{y}|\text{x})p_{g}(\text{x})\) denotes the marginal distribution of class y.  
FID calculates the Wasserstein-2 distance between original images and generated images in a feature space. A low score of FID indicates that the results have better quality. The FID is defined as follows:  
\[\text{FID}=\left\|\mu_{r}-\mu_{g}\right\|^{2}+Tr\left(\Sigma_{r}+\Sigma_{g}-2 \left(\Sigma_{r}\Sigma_{g}\right)^{1/2}\right) \tag{11}\]  
where \(\mu_{r}\) is the mean of the real image feature vector, \(\mu_{g}\) is the mean of the generated image feature vector, \(Tr\) is the  
The evaluation metrics are evaluated to compare generated landscape paintings on samples of Dwelling in the Fuchun Mountains; the comparison results are shown in Table 2. MUNIT samples from both a content and style space and reconstructs them to generate final results. Due to the complex structure and style features of landscape paintings, the generated results are not satisfactory. MUNIT has the lowest IS and highest FID. The generated landscape paintings of NICE-GAN are similar to the ink wash style; however, the details are not recovered well. Although there are some problems, the generated landscape paintings have improvements both in style and content compared to MUNIT; thus, the NICE-GAN has a higher IS and lower FID than MUNIT. U-GAT-IT incorporates attention to maintaining the basic structure and detailed information of the generated landscape paintings, which can generate paintings with more details than NICE-GAN. Although some brush strokes are still lost, the generated landscape paintings are better than NICE-GAN in terms of content and style. The IS of U-GAT-IT is higher than NICE-GAN and the FID is lower. CycleGAN uses cycle consistency loss to ensure the accuracy of translation results. The generated landscape paintings are similar to the content of landscape photos and have an ink wash style; however, some brush strokes and details are lost. The CycleGAN has a higher IS and lower FID than U-GAT-IT. CUT is used as a baseline model, which uses contrastive loss instead of cycle consistency loss. And the CUT only uses a generator and a discriminator to achieve the image translation. The generated results of CUT are better than other comparison models both in content and style and has a higher IS and lower FID than others. However, the details of the generated landscape paintings are still blurred and the style is unclear by directly using CUT.