Unsupervised Landscape Painting Style Transfer Network with Multiscale Semantic Information. (English)

This paper proposes CCME- GAN (circulatory correction multiscale evaluation-generative adversarial networks) based on the cycle consistency loss, aiming at the problems of texture clutter and poor quality of generated images when the generative adversarial network of image conversion class is dealing with the task of unsupervised style transfer. Firstly, in the design of the network architecture, a multiscale evaluation network architecture based on the three-layer semantic information of images is proposed to enhance the transfer effect from the source domain to the target domain. Secondly, in the improvement of the loss function, a multiscale adversarial loss and a cyclic correction loss are proposed to guide the optimization iteration direction of the model with a stricter target, and generate pictures with better visual quality. Finally, in order to prevent the problem of pattern collapse, this paper adds an attention mechanism in the encoding stage of style features to extract important feature information, and then introduces the ACON activation function in each stage of the network to strengthen the nonlinear expression ability of the network and avoid neuron necrosis. The experimental results show that the FID value of this paper method is reduced by 21.80% and 34.33% compared with CycleGAN and ACL-GAN on the landscape painting style migration dataset. In addition, in order to verify the generalization ability of the model, the generalization experiments are compared on two public datasets, Vangogh2Photo, and Monet2Photo and the FID values are decreased by 7.58%, 18.14% and 4.65%, 6.99% respectively. [ABSTRACT FROM AUTHOR]

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