@inproceedings{9e09c3ea696d4c2f8b300ce0f04f7fcf,
title = "SS-GANs: Text-to-image via stage by stage generative adversarial networks",
abstract = "Realistic text-to-image synthesis has achieved great improvements in recent years. However, most work ignores the relationship between low and high resolution and prefers to adopt identical module in different stages. It is obviously inappropriate because the differences in various generation stages are huge. Therefore, we propose a novel structure of network named SS-GANs, in which specific modules are added in different stages to satisfy the unique requirements. In addition, we also explore an effective training way named coordinated train and a simple negative sample selection mechanism. Lastly, we train our model on Oxford-102 dataset, which outperforms the state-of-the-art models.",
keywords = "Coordinated train, Different stages, Negative samples, Text-to-image",
author = "Ming Tian and Yuting Xue and Chunna Tian and Lei Wang and Donghu Deng and Wei Wei",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 ; Conference date: 08-11-2019 Through 11-11-2019",
year = "2019",
doi = "10.1007/978-3-030-31723-2_40",
language = "英语",
isbn = "9783030317225",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "475--486",
editor = "Zhouchen Lin and Liang Wang and Tieniu Tan and Jian Yang and Guangming Shi and Nanning Zheng and Xilin Chen and Yanning Zhang",
booktitle = "Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II",
}