# A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters
## 2 Related Work  
The work of map-style transfer focuses on transferring texture features in the early stage. However it does not explicitly address the style transfer task itself, resulting in less-than-ideal outcomes. With the development of deep learning technologies, some work [24] trains images with neural networks to make the image's style closer to the target style. But, these methods are computationally intensive and not suitable for real-time map generation, which tends to distortions in certain image data during transfer. [PERSON] et al. [25] simplify the style transfer problem as an optimization problem within a single neural network, of which the texture model based on deep image models was able to generate new images by matching feature representations of example images via an image search. However, it is not well suited for photos. Subsequently, [PERSON] et al. [26] expand on [PERSON] work by replacing the input random noise image with a target image and incorporating a forward autoencoder network to model the style transfer process. [PERSON] et al. [27] address the limitations of [PERSON] model by constraining the output within a specific color space to suppress deformations and maintain the realism of photos. The constraint is fully differentiable, enabling better preservation of style information from reference images without introducing distortions caused by deformations. [PERSON] et al. [28] use feature transformations such as Whitening and Coloring Transform (WCT) to directly match the statistical properties of content feature statistics with those of style images in the deep feature space. [PERSON] et al. [29] improve the WCT-based method by introducing stylization and smoothing steps to tackle structural artifacts in the output of the WCT algorithm. However, the WCT-based approach still have the potential to produce spatial distortions or unrealistic artifacts. [PERSON] et al. [30] suggest incorporating wavelet transform into neural networks based on WCT, enabling features to retain both structural information and statistical properties of the VGG space, thereby addressing the aforementioned limitations. Recent work tends to use GANs to implement map-style transfer, especially CycleGAN [31]. However, there are challenges pertaining to image accuracy and real-time processing, which motivates us to enhance the CycleGAN for map-style transfer.  
WA is a technique which fuses the deep learning models and aims to improve the model. Some recent work [32] uses Stochastic Weighted Averaging (SWA) [33] in CycleGANs [34; 35], which enhances the capabilities of generators. Inspired by checkpoint averaging [36; 37], [PERSON] et al. [33] propose SWA. Using the mean of multiple models on the training trajectory can help find a better-performing network with no additional computational overhead compared to training a single model. When SGD convergence is good, the checkpoint of SWA sampling is concentrated around the optimal solution. Using SWA operations for these checkpoints helps optimize the training network. Similar to FGE [38; 39; 40], using the cyclic learning rate [33; 41], models that are spatially close to each other but produce different predictions can be collected. Some recent work proposes variations on SWA that allow SWA to be adapted to more complex application scenarios and have more efficient rates. We tend to combine SWAD and SWA with CycleGANs to improve the performance of the navigation maps.