Generative Adversarial Text to Image Synthesis

Scott ReedZeynep Akata , Xinchen Yan,

Lajanugen Logeswaran, Bernt Schiele and Honglak Lee


Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories such as faces, album covers, room interiors and flowers. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions.

PaperCode and Data

  • If you use our code, please cite:
@inproceedings {RAYLLS16, 	
title = {Generative Adversarial Text to Image Synthesis},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2016},
author = {Scott Reed and Zeynep Akata and Xinchen Yan and Lajanugen Logeswaran and
Bernt Schiele and Honglak Lee} }