Art Image Generation System Based on Artificial Intelligence

In order to generate high-quality images for expanding data sets and image classification, this paper proposes a method of art image generation system based on artificial intelligence. Combined with the advantages of the current image generation domain model, a two-stage image generation method with intermediate inputs is introduced. The main process of this method is use a basic GAN model to fit the features of the output image through a feature capture network (classification network). The generator with generating classification feature is fused into a basic GAN model to form a new GAN model for generating pictures. The experimental results show that the FID score of the basic model is 8.14, and the FID score of the model in this paper is 5.27. According to the measurement standard of FID, the lower the FID score is, the better the picture quality is. Verified that the two-stage image generation model outperforms the basic model in generating the MNIST dataset, indicating the effectiveness of the model. Conclusion: the model using this method has stronger pattern generation ability than the basic model.