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.
The first author Ming Tian is master candidate.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61571354 and 61671385. In part by China Post doctoral Science Foundation under Grant 158201.
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Tian, M., Xue, Y., Tian, C., Wang, L., Deng, D., Wei, W. (2019). SS-GANs: Text-to-Image via Stage by Stage Generative Adversarial Networks. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_40
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DOI: https://doi.org/10.1007/978-3-030-31723-2_40
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