Text to Image Synthesis Using Two-Stage Generation and Two-Stage Discrimination

In this paper, the method of two-stage generation and two-stage discrimination (2G2D) is proposed to generate high-resolution and more realistic images. It is a simple but effective way to synthesize images based on text descriptions. Our method generates the refined foreground image in the first stage, and then combines the text description to generate the final high-resolution image in second stage. We demonstrate the performance of the proposed method on the Caltech-UCSD Birds (CUB) dataset. Through the experimental results, our model can improve the resolution and the authenticity of content of the synthetic image better than the existing state-of-the-art methods.