  Text-to-image synthesis aims to generate a photo-realistic image from a given
natural language description. Previous works have made significant progress
with Generative Adversarial Networks (GANs). Nonetheless, it is still hard to
generate intact objects or clear textures (Fig 1). To address this issue, we
propose Feature-Aware Generative Adversarial Network (FA-GAN) to synthesize a
high-quality image by integrating two techniques: a self-supervised
discriminator and a feature-aware loss. First, we design a self-supervised
discriminator with an auxiliary decoder so that the discriminator can extract
better representation. Secondly, we introduce a feature-aware loss to provide
the generator more direct supervision by employing the feature representation
from the self-supervised discriminator. Experiments on the MS-COCO dataset show
that our proposed method significantly advances the state-of-the-art FID score
from 28.92 to 24.58.
