Image style migration method based on transformer generative adversarial network combined with contrast learning strategy
Xing Chen · Journal of Physics Conference Series · 2024
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.