# A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters
## 4 Experiments
### Experiment Results  
#### 4.4.1 Experiment Results of Prevailing and Proposed Methods on Google Maps Dataset  
In the initial phase, we conduct 200 rounds of training. The training results show that after approximately 100 rounds, all indicators display minimal fluctuations, indicating convergence. Therefore, we present the results and various parameter metrics after 100 rounds of training. To ensure the completeness of the model, we conduct forward and reverse experiments for each set of methods. This means that the model should be able to transform from satellite map-style class-A data to navigation map-style class-B-style data and vice versa. Table 1 presents the parameter values for the various methods used in the four comparative experiments. \(IS\)-\(B\) refers to the inception score of the generated class-B-style data in the forward process (satellite map to navigation map). \(IS\)-\(A\) refers to  
Figure 6: The generation results of SWAD on MNIST after 100 rounds of training (results sampled every 10 rounds). the inception score of the generated class-A-style data in the reverse process (navigation map to satellite map). First, from Table 3, the combination of CycleGAN with SWAD performs best than SWA and other methods. Compared to SWA, when SWAD is deployed on the generators, the \(FID\) indicator decreases by 55.8% and the \(IS\)-\(B\) indicator has increased by 28.9%. It means that the gap of SWAD between the generated data and real data decreases significantly compared to SWA or SGD. At the same time, the quality of both A-class-style and B-class-style generated data improves considerably, which is a favorable outcome for GANs. Moreover, when PSWA [37] is deployed on both the generators, it improves the \(IS\)-\(A\) indicator, but it performs less well on other indicators such as \(FID\). Second, the method works better when deployed on generators than deployed on both generators and discriminators. When SWAD is deployed only on the generators, the gap between generated data and real data decreases significantly compared to the previous experiments. Although it is still larger than the baseline, the quality of generated A-class data is even higher, and the quality of generated B-class data also improves considerably. This indicates that deploying SWAD on the generator alone yields better results than deploying it simultaneously. When SWAD is simultaneously deployed on both the generator and discriminator, it amplifies the gap between generated data and real data. This is because the model inherently involves an adversarial iterative process, where the generator and discriminator compete with each other to achieve training effectiveness. Deploying the optimization method on both sides inevitably leads to one side being weaker and the other stronger, which is clearly not conducive to training. Hence, the elevated value of this indicator is understandable. Third, compared to SGD, the indicators of SWA related to the quality of generation are better, which indicates that SWDA improves the resolution of generated images. Additionally, the effect of SWAD on \(FID\), \(IS\)-\(A\), and \(IS\)-\(B\) is higher than SGD, but other SWA variants still fall short of the SGD indicators. This further highlights that the network's adversarial performance is not solely dependent on the optimization method. Figure 7 demonstrates the loss reduction process of various methods during the training process. Each column corresponds to a different optimization method. Within each column, the top section represents the loss when discriminating A-class images (\(D_{A}\)), the middle section represents the loss when discriminating B-class-style images (\(D_{B}\)), and the bottom section represents the total loss of the generator.