Text to Image Synthesis Based on Multiple Discrimination

We propose a novel and simple text-to-image synthesizer (MD-GAN) using multiple discrimination. Based on the Generative Adversarial Network (GAN), we introduce segmentation images to the discriminator to ensure the improvement of discrimination ability. The improvement of discrimination ability will enhance the generator’s generating ability, thus obtaining high-resolution results. Experiments well validate the outstanding performance of our algorithm. On CUB dataset, our inception score is 27.7% and 1.7% higher than GAN-CLS-INT and GAWWN, respectively. On the flower dataset, it further outplays GAN-CLS-INT and StackGAN by 21.8% and 1.25%, respectively. At the same time, our model is more concise in structure, and its training time is only half that of StackGAN.