Image style migration method based on transformer generative adversarial network combined with contrast learning strategy

Abstract Aiming at the problems of large loss of image patch embedding information and lack of stylized features in the generated image during transformer style migration, a style migration model combining contrast learning with transformer backbone generative adversarial network is established. Firstly, overlapping patch embedding is used to serialize the input images to get richer features; then the style and content contrast loss of contrast learning is used to mine the non-significant information between stylized images to reduce the style and content loss; finally, a multi-scale discriminator discriminates the true and false of stylized images to optimize the generator to get more stylistic features. The results on MS-COCO and WikiArt datasets show that the model improves the effectiveness and transformation efficacy of stylized images in style migration.