Exploration of generative adversarial network optimization and image stylization creation

This project aims to improve the quality and efficiency of image stylization by improving the generative adversarial network (GAN) technology. The research path of style transfer technology is improved by adopting innovative GAN model structure and multi-optimization methods, such as adjusting the learning rate, implementing gradient clipping, and selecting various optimization algorithms. The WikiArt dataset is used to carry out stylized learning, and the effectiveness of various optimization strategies is compared to evaluate their content maintenance and style simulation performance. The results show that the improvement measures significantly enhance the stylization performance of the image, especially in terms of style degradation and content loss. The conclusion shows that the optimized generative adversarial network model significantly improves the accuracy and visual effect of image stylization, showing its application potential in the fields of artistic creation and visual content editing.