# Cyclic Consistent Image Style Transformation: From Model to System
## 3 Image Style Transfer Technology Based on GAN
### Comparison and Improvement of an Image Style Transfer System Based on CycleGAN  
CycleGAN represents an advancement over traditional GAN architectures, incorporating key improvements such as cyclic consistency, the ability to operate with non-paired data, multi-modal output, and the integration of identity loss. Compared to other GAN variants like CoGAN [37], BiGAN/ALI [38], and SimGAN [39], CycleGAN is particularly well-suited for image style transfer across different domains. The following outlines the differences and improvements of CycleGAN over these other models:  
Firstly, compared to CoGAN, CycleGAN leverages cyclic consistency loss, which ensures that an image, once transformed to another domain and then back, retains its original content. CoGAN, lacking this loss function, may suffer from content loss or distortion. Moreover, CycleGAN employs identity loss, allowing the generator to output the original image without transformation when the input is already in the target domain, thus preserving the image's style. CoGAN does not utilize identity loss, leading to potential over-transformation or blending of styles. Additionally, CycleGAN supports multi-modal output, enabling the generation of images in various styles based on the input's characteristics and random noise. In contrast, CoGAN only allows for single-modal output, producing images in just one style.  
Secondly, in comparison to BiGAN/ALI, CycleGAN avoids using an encoder to map input images to a latent space, instead directly transforming the input image to the target domain using a generator. This approach reduces model complexity and computation while avoiding errors and information loss introduced by the encoder. Unlike BiGAN/ALI, which relies on supervised data pairs for training, CycleGAN can utilize unsupervised datasets, allowing for broader application and more diverse data sources, while also mitigating issues related to incomplete or inaccurate data. Furthermore, CycleGAN supports multi-domain transformations, enabling the generator to process multiple source and target domains simultaneously, whereas BiGAN/ALI is limited to two-domain conversions.  
Lastly, when compared to SimGAN, CycleGAN employs adversarial loss and a discriminator to train the generator, improving the quality and realism of the generated images by ensuring they can fool the discriminator. SimGAN, on the other hand, uses perceptual loss and local perceptual discriminators to train the generator, which can result in images that are similar to those in the real image domain but may be lacking in quality and authenticity. CycleGAN's use of cyclic consistency loss and identity loss helps maintain both the content and style of the original image. In contrast, SimGAN relies on self-supervised learning, which, while gradually enhancing the synthetic image's defects, might alter the original content and style. Moreover, CycleGAN allows for conversion between any two different image domains without requiring paired data, whereas SimGAN is restricted to converting synthetic images to real images, necessitating synthetic data as the input.  
Building on these principles, this study proposes an image style transfer system based on CycleGAN, which not only achieves unique artistic effects by applying the style of one image to another but also enhances adaptability across various datasets and broadens the system's application scenarios.