Abstract:  An improved anime style transfer algorithm for generative adversarial networks is proposed to address the issues of missing detail structure, color shifting, and semantic content artifacts. Firstly, a feature transformation module is constructed by combining channel shuffle operations with improved inverted residual blocks to enhance the local feature attributes of the image, and an efficient attention mechanism is incorporated to further improve the style feature representation capability. Secondly, the style loss function is modified to suppress the influence of brightness and color variations on high-frequency texture learning. Finally, content images containing random noise are fed into the generator and a spectral normalization constraint is applied to the discriminator network to address the issue of mode collapse. The experimental results demonstrate that the image generated by the proposed method is richer in detail than other algorithms, and effectively avoiding the occurrence of artifacts and color shifting, so that the generated image will have a greater sense of realism, achieving style Frechet inception distance of 154.61 and 115.64, respectively.

Key words: generative adversarial networks, style transfer, residual block, style loss