# 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
To compare the convergence speed and oscillation level of different optimization methods during training, we perform statistical analysis on the total loss of generator shown at the bottom of Figure 7 and document the results in Table 4. In order to standardize the convergence criteria, we establish the following guidelines:  
1. Convergence is considered when the amplitude of the loss curve does not exceed 10% of the maximum loss value, and the training round at this point is identified as the convergence time. 2. We record the oscillation counts equal to or greater than 10% of the maximum loss value to assess the extent of oscillation during training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **FID** & **Decreasing Rate of FID**1  & **IS-A** & **IS-B** \\ \hline SWA & 194.970 & 0 & 4.328 & 2.334 \\ SWA (generators and discriminators) 2 & 331.962 & \(-\)70.3\% & 3.897 & 2.097 \\ PSWA & 246.689 & \(-\)26.5\% & 4.756 & 1.796 \\ SGD & 89.147 & 54.3\% & 3.696 & 3.004 \\ SWAD & 86.274 & 55.8\% & 3.892 & 3.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance indicators based on prevailing and proposed methods on test dataset. As shown in Table 4, the convergence speed of SWA is nearly identical to that of SGD, but it significantly reduces the degree of oscillation. On the other hand, the PSWA method not only converges slower but also intensifies the oscillation. In contrast, SWAD demonstrates better performance by significantly accelerating the convergence speed and reducing the degree of oscillation, leading to a more stable training process. Figure 7 indicates that the SWA is more effective in identifying the optimal solution compared to the baseline, enabling it to reach the lowest gradient more rapidly. However, this also leads to a higher generator loss for the same batch compared to the baseline. In panel (d), both the generator and discriminator exhibit a more balanced performance, ultimately surpassing the baseline. The experimental results further demonstrate that the SWA optimization method effectively addresses the two longstanding issues encountered in map-style transfer tasks: time-consuming processes and poor quality. The total loss of the generator consists of three components: \(G_{GAN_{loss}}\)(composed solely of the loss from the \(G_{A2B}\) and \(G_{B2A}\) generators), \(G_{cycle_{loss}}\), and \(G_{identity_{loss}}\). The loss functions for these components are depicted in Figure 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Convergence Rounds** & **Oscillation Counts** \\ \hline SWA & 42 k & 40 \\ SWA (generators and discriminators) & 35 k & 35 \\ PSWA & 48 k & 51 \\ SGD & 36 k & 46 \\ SWAD & 26 k & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The convergence rounds and oscillation counts for each optimization method. Figure 7: Training loss for each optimization method. (**Top**) loss of discriminator \(A\); (**Middle**) loss of discriminator \(B\); and (**Bottom**) total loss of generator.