{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stackgan-realistic-image-synthesis-with","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","arxiv_id":"1710.10916","date":"2017-10-19","proceeding":null,"authors":["Han Zhang","Tao Xu","Hongsheng Li","Shaoting Zhang","Xiaogang Wang","Xiaolei Huang","Dimitris Metaxas"],"abstract":"Although Generative Adversarial Networks (GANs) have shown remarkable success\nin various tasks, they still face challenges in generating high quality images.\nIn this paper, we propose Stacked Generative Adversarial Networks (StackGAN)\naiming at generating high-resolution photo-realistic images. First, we propose\na two-stage generative adversarial network architecture, StackGAN-v1, for\ntext-to-image synthesis. The Stage-I GAN sketches the primitive shape and\ncolors of the object based on given text description, yielding low-resolution\nimages. The Stage-II GAN takes Stage-I results and text descriptions as inputs,\nand generates high-resolution images with photo-realistic details. Second, an\nadvanced multi-stage generative adversarial network architecture, StackGAN-v2,\nis proposed for both conditional and unconditional generative tasks. Our\nStackGAN-v2 consists of multiple generators and discriminators in a tree-like\nstructure; images at multiple scales corresponding to the same scene are\ngenerated from different branches of the tree. StackGAN-v2 shows more stable\ntraining behavior than StackGAN-v1 by jointly approximating multiple\ndistributions. Extensive experiments demonstrate that the proposed stacked\ngenerative adversarial networks significantly outperform other state-of-the-art\nmethods in generating photo-realistic images.","url_abs":"http://arxiv.org/abs/1710.10916v3","url_pdf":"http://arxiv.org/pdf/1710.10916v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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