# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 2 Related Work
### Facial Style Transfer  
Facial style transfer necessitates precise techniques to extract diverse facial features, ensuring successful application of the migrated style onto the original image without distorting the characters [14]. Given the unique characteristics of facial structures like eyes, nose, and lips, the algorithm's requirements for style migration are more stringent compared to other image types.  
A novel universal face photo-sketch style transfer method that does not require training with source domain images was introduced by [35]. Ref. [36] proposed the Fusion of STyles (FIST) network for facial images, utilizing pre-trained multipath style transfer networks to address data volume challenges in training and fuse multiple styles at the output. Ref. [37] presented a technique for transferring painting styles onto head portraits, imposing spatial constraints through local color distribution transfers. Ref. [38] proposed BlendGAN for arbitrary stylized face generation, leveraging a flexible blending strategy and a generic artistic dataset, along with a weighted blending module for implicit face and style representation blending. Ref. [39] combined generative adversarial networks with attention mechanisms to address these challenges. Ref. [40] introduced a two-stage geometric style transfer method dedicated to face portraits, simultaneously transferring statistical and structural styles. Ref. [41] performed incremental facial exaggeration from real images to caricatures using encoder and generator latent spaces. Ref. [42] presented an asymmetric double-stream generative adversarial network (ADS-GAN) to address issues like facial deformation and contour missing during style transfer in portrait photos. Ref. [43] modeled caricature generation as a weakly paired image-to-image translation and proposed CariGAN to address related issues and used manually annotated facial landmarks as an additional constraint to ensure reasonable exaggeration and facial deformation in the generated images. Ref. [44] introduced XGAN, a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. Ref. [45] proposed a multi-exaggeration warped network to learn the distribution-level mapping from photos to facial exaggerations, which makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. Ref. [46] proposed ETCari, a novel weakly supervised exaggerationtransfer network. ETCari enables the learning of diverse exaggeration caricature styles from various artists, better meeting individual customization requirements and achieving diversified exaggeration while retaining identity features.  
However, many of the stylized face photos generated by these methods encounter problems like texture detail neglect, unnatural color transitions, and ineffective artifact handling. To address these challenges, this paper proposes an Improved Detail-Enhancement CycleGAN approach.