# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 2 Related Work  
### Generative Adversarial Network  
**Paired data**. Currently, GAN-based facial style transfer algorithms can be broadly categorized into two types: those based on paired data and those based on unpaired data [25]. The first type of method places significant demands on the dataset, requiring each original image to have a corresponding target style image. During training, the generative network is constrained by comparing the difference between the generated image and the real target image [26]. A representative method of this type is Pix2 pix [27], where the discriminative network essentially functions as a binary categorization network, determining whether the input image belongs to the same style as the real image. Building upon Pix2 Pix, ref. [28] introduced Pix2 PixHD to address high-resolution reconstruction challenges in semantic segmentation. This approach proposes synthetic reconstruction based on segmentation information, tackling the loss of detail and texture in ultra-high-resolution image synthesis. Subsequently, Cartoon GAN, also based on Pix2 Pix, was proposed to cartoonize real-world images using adversarial networks [29]. It exhibits promising results for images like landscapes and buildings. However, methods reliant on pairwise datasets face a common limitation--they struggle to gather a large volume of pairwise data in real-world scenarios, presenting a significant challenge for such approaches.  
Figure 1: Problems with facial style transfer at this stage (**a–d**). The red boxes in the figure indicate the defects in the generated images.  
**Unpaired data**. The emergence of the second type of method aimed to overcome the limitations of the previous approaches. This method operates on the assumption of a common implicit space for two styles of images. By mapping an image from style A to this implicit space, it can be transformed into an image with style B using the corresponding style decoder. CycleGAN [20], discoGAN [30], and dualGAN [31] leverage the concept of reconstruction to constrain the generative network. In the absence of a real image counterpart, an effective model can transition an image from style A to style B and back to A, ensuring content preservation through the reconstruction process. Of these methods, ref. [32] introduced DualGGAN, a Transformer style transfer network based on dual generators and the fusion of relative position encoding. This addresses the background penetration issue often encountered in unsupervised style transfer algorithms. Ref. [33] presented FacialGAN, a novel framework enabling rich style transfers and interactive manipulation of facial attributes simultaneously. Additionally, ref. [34] proposed StyleCariGAN, an automated system for creating realistic and detailed caricatures from input photos with optional controls for shape exaggeration and color stylization.  
Among the methods utilizing unpaired datasets, CycleGAN [20] introduced by [PERSON] et al. stands out as a landmark. The core concept involves training two pairs of generator-discriminator models to facilitate image transformation between different domains. The training process emphasizes cyclic consistency, supported by a corresponding loss function designed to enforce this consistency. This ensures that the image generated when transitioning from the source domain to the target domain and back remains as faithful as possible to the original image.