CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation

Generating photo-realistic images from a text description is a challenging problem in computer vision. Previ- ous works have shown promising performance to generate synthetic images conditional on text by Generative Adver- sarial Networks (GANs). In this paper, we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images.