  This paper presents a new Text-to-Image generation model, named Distribution
Regularization Generative Adversarial Network (DR-GAN), to generate images from
text descriptions from improved distribution learning. In DR-GAN, we introduce
two novel modules: a Semantic Disentangling Module (SDM) and a Distribution
Normalization Module (DNM). SDM combines the spatial self-attention mechanism
and a new Semantic Disentangling Loss (SDL) to help the generator distill key
semantic information for the image generation. DNM uses a Variational
Auto-Encoder (VAE) to normalize and denoise the image latent distribution,
which can help the discriminator better distinguish synthesized images from
real images. DNM also adopts a Distribution Adversarial Loss (DAL) to guide the
generator to align with normalized real image distributions in the latent
space. Extensive experiments on two public datasets demonstrated that our
DR-GAN achieved a competitive performance in the Text-to-Image task.
