Xinlong Wu1, Desheng Zheng1,*, Kexin Zhang1, Yanling Lai1, Zhifeng Liu1, Zhihong Zhang2
Journal of Quantum Computing, Vol.3, No.4, pp. 127-136, 2021, DOI:10.32604/jqc.2021.017251
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… More >
Login
Register
