# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 1 Introduction  
Facial style transfer, a humorous art form, captivates audiences with its unique interpretations of human features [1]. Artists use abstract strokes, fluid lines, and exaggerated expressions to create stylized portraits, revealing personality and emotion in ways traditional portraiture might miss [2]. However, this process is complex and heavily dependent on the artist's creative inspiration, requiring skill, time, and multiple iterations [3]. In the era of self-media, where content creation is ubiquitous, this traditional approach struggles to meet the demand for instant, personalized content [4]. With advancements in deep learning and image-to-image translation, AI-powered facial style transfer has gained attention. These technologies promise near-instantaneous stylization, democratizing access to this art form and allowing users to experiment with various styles effortlessly [5]. The main challenge lies in balancing style transformation with identity preservation. Algorithms must accurately replicate artistic styles while maintainingthe subject's core facial features. This requires sophisticated techniques to capture style essences and apply them without compromising recognizability [6]. The main difficulty in facial style transfer lies in both the stylistic transformation of the input portrait features and maintaining the identity consistency of the generated image with the input photo [7; 8]. Before the emergence of deep learning, the facial style transfer problem was primarily addressed using traditional methods such as interactive-based techniques, rule-based five-feature contour deformation methods, example-based learning methods, and database-based style matching methods. For instance, the face shape and the five-feature contour of the face are depicted by [9] through the positioning of key facial points, with the contour being corrected using geometric template detection to automatically generate a line drawing. Similarly, the image is chunked directly by [10] with a sliding window to divide the image into overlapping blocks of the same size, and a Markov network model is employed to find the stylized image block that best matches the face image block, with these blocks being synthesized to form a complete stylized face image. However, most of these methods cannot achieve a fully automated facial stylization migration process and suffer from complex rules, single-generation effects, and a lack of creativity. With the development of deep learning, these problems are being resolved one by one [11; 12; 13]. Deep learning-based methods for facial style transfer can be categorized into two main types: neural style transfer (NST) and generative adversarial network (GAN)-based methods [14; 15]. [PERSON]. [16] pioneered the NST approach, using convolutional neural networks (CNNs) to separate and reorganize image content and style. With NST, an image's style can be modified while preserving its content, achieving artistic re-creation. However, numerous experiments have shown that CNN-based methods tend to focus primarily on the transfer of image texture, color, and pixel-level style features. Additionally, CNNs are often slow to train, insensitive to fine details, and struggle to achieve comprehensive stylization of face images [17]. In recent years, generative adversarial networks (GANs) have garnered widespread attention due to their powerful image generation capabilities [18; 19]. GAN is an unsupervised learning framework proposed by [PERSON] et al. in 2014, consisting of a generator G and a discriminator D. The generator converts random noise images into target images, while the discriminator judges whether an image is real or generated by the generator. When the generator and discriminator reach a balance through adversarial training, the generator can produce realistic fake images.