Quantum mechanics

Linguistics

Philosophy

Statistics

Similarity (geometry)

Generative adversarial network

Power (physics)

Computer science

Natural language processing

Boosting (machine learning)

Physics

Generative grammar

Image synthesis

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Text generation

Artificial intelligence

Geometry

Matching (statistics)

Generator (circuit theory)

Deep learning

Word (group theory)

Computer vision

Adversarial system

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 sub-regions 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.

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks