@Article{jqc.2021.017251,
AUTHOR = {Xinlong Wu, Desheng Zheng, Kexin Zhang, Yanling Lai, Zhifeng Liu, Zhihong Zhang},
TITLE = {Implementation of Art Pictures Style Conversion with GAN},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {127--136},
URL = {http://www.techscience.com/jqc/v3n4/46223},
ISSN = {2579-0145},
ABSTRACT = {Image conversion refers to converting an image from one style to 
another and ensuring that the content of the image remains unchanged. Using 
Generative Adversarial Networks (GAN) for image conversion can achieve good 
results. However, if there are enough samples, any image in the target domain can 
be mapped to the same set of inputs. On this basis, the Cycle Consistency 
Generative Adversarial Network (CycleGAN) was developed. This article verifies 
and discusses the advantages and disadvantages of the CycleGAN model in image 
style conversion. CycleGAN uses two generator networks and two discriminator 
networks. The purpose is to learn the mapping relationship and inverse mapping 
relationship between the source domain and the target domain. It can reduce the 
mapping and improve the quality of the generated image. Through the idea of loop, 
the loss of information in image style conversion is reduced. When evaluating the 
results of the experiment, the degree of retention of the input image content will 
be judged. Through the experimental results, CycleGAN can understand the 
artist’s overall artistic style and successfully convert real landscape paintings. The 
advantage is that most of the content of the original picture can be retained, and 
only the texture line of the picture is changed to a level similar to the artist’s style.},
DOI = {10.32604/jqc.2021.017251}
}