Image neural style transfer: A review

Traditional methods of style transfer emphasize primarily the transfer of artistic styles. In recent years, style transfer has expanded beyond the realm of artistic expression to encompass fields such as medicine, industry, and literature. Currently, the style transfer algorithm generating the most attention is the Generative Adversarial Networks (GANs) approach. In this paper, we provide a summary and analysis of the style transfer algorithm based on convolutional neural networks from the perspective of GANs. We review the development process from traditional style transfer algorithms to convolutional neural network-based ones, evaluate their effectiveness and application value, and discuss future research directions and challenges for generative adversarial network-based style transfer algorithms.