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    Implementation of Art Pictures Style Conversion with GAN

    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… More >

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