StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Paper summary Problem ------------ Text to image Contributions ----------------- * Images are more photo realistic and higher resolution then previous methods * Stacked generative model Approach ------------- 2 stage process: 1. Text-to-image: generates low resolution image with primitive shape and color. 2. low-to-hi-res: using low res image and text, generates hi res image. adding details and sharpening the edges. https://pbs.twimg.com/media/Cziw6bfWgAAh3Yg.jpg Datasets -------------- * CUB - Birds * Oxford-102 - Flowers Results -------- https://cdn-images-1.medium.com/max/1012/1*sIphVx4tqaXJxtnZNt3JWA.png Criticism/ Questions ------------------- * Is it possible the resulting images are replicas of images in the original dataset? To what extent does the model "hallucinate" new images?
arxiv.org
scholar.google.com
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Huang, Xiaolei and Wang, Xiaogang and Metaxas, Dimitris N.
arXiv e-Print archive - 2016 via Bibsonomy
Keywords: dblp


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