Text To Image Synthesis With Erudite Generative Adversarial Networks

In this paper, an Erudite Generative Adversarial Networks (EruditeGAN) is proposed for the text-to-image synthesis task. By introducing additional image distribution related to the original image into the network structure, the entire network can learn more about the image distribution and become more knowledgeable. In this case, it can be more clear about the distribution of the image that needs to be synthesized and finally synthesize high-quality results. Experiments well validate the effectiveness of our method and demonstrate the different effects of different distribution situations on the final results. According to the quantitative results of Fr??chet Inception Distance (FID) and R-precision, our method's comprehensive score is the best, which reflects our results are closer to the real image effect.