AttnGAN: Fine-Grained Text to Image Generation
with Attentional Generative Adversarial Networks
Abstract
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven,
multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant
words in the natural language description. In addition, a
deep attentional multimodal similarity model is proposed to
compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and
170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that
the layered attentional GAN is able to automatically select
the condition at the word level for generating different parts
of the image