ARCHIVES

1. T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, X. He, Attngan: Fine-grained text to image generation with attentional generative
adversarial networks, in: Proceedings of the IEEE Computer Vision and Pattern Recognition, 2017, pp. 1316–1324.
2. H. Zhang, T. Xu, H. Li, Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks, in: Proceedings of
the IEEE International Conference on Computer Vision, 2016, pp. 5907–5915.
3. H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, D. N. Metaxas, Stackgan++: Realistic image synthesis with stacked generative
adversarial networks, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2017) 1947–1962.
4. Z. Zhang, Y. Xie, L. Yang, Photographic text-to-image synthesis with a hierarchically-nested adversarial network, in: Proceedings of the
IEEE Computer Vision and Pattern Recognition, 2018, pp. 6199–6208
5. L. Gao, D. Chen, J. Song, X. Xu, D. Zhang, H. T. Shen, Perceptual pyramid adversarial networks for text-to-image synthesis, in: Proceedings
of the AAAI Conference on Artificial Intelligence, 2019, pp. 8312–8319.
6. M. Yuan, Y. Peng, Bridge-gan: Interpretable representation learning for text-to-image synthesis, IEEE Transactions on Circuits and
Systems for Video Technology (2019) 1–1.
7. T. Qiao, J. Zhang, D. Xu, D. Tao, Mirrorgan: Learning textto-image generation by redescription, in: Proceedings of the IEEE Computer
Vision and Pattern Recognition, 2019, pp. 1505–1514.
8. Q. Lao, M. Havaei, A. Pesaranghader, F. Dutil, L. Di-Jorio, T. Fevens, Dual adversarial inference for text-to-image synthesis, in:
Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 7567–7576.
9. M. Zhu, P. Pan, W. Chen, Y. Yandg, Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis, in: Proceedings
of the IEEE Computer Vision and Pattern Recognition, 2019, pp. 5802–5810.
10. Mehdi Mirza, Simon Osindero, Conditional Generative Adversarial in arxiv 1411
11. K. J. Joseph, A. Pal, S. Rajanala, V. N. Balasubramanian, C4synth: Cross-caption cycle-consistent text-to-image synthesis, in: IEEE Winter
Conference on Applications of Computer Vision, 2018, pp. 358–366.
12. T. Hinz, S. Heinrich, S. Wermter, Semantic object accuracy for generative text-to-image synthesis, IEEE Transactions on Pattern Analysis
and Machine Intelligence (2020).
13. S. Hong, D. Yang, J. Choi, H. Lee, Inferring semantic layout for hierarchical text-to-image synthesis, in: Proceedings of the IEEE Computer
Vision and Pattern Recognition, 2018, pp. 7986–7994.
14. W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu, J. Gao, Object-driven text-to-image synthesis via adversarial training, in: Proceedings
of the IEEE Computer Vision and Pattern Recognition, 2019, pp. 12166–12174.
15. H. Zhang, J. Y. Koh, J. Baldridge, H. Lee, Y. Yang, Cross-modal contrastive learning for text-to-image generation, arXiv:2101.04702
(2021).
16. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, Y. Bengio, Gen erative adversarial nets,
in: Advances in Neural Information Processing Systems, 2014, pp. 26722680.
17. C. Ledig, L. Theis, F. Huszar, J. A. Caballero, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, Photo-realistic single image super-resolution
using a generative adversarial network, in: Proceedings of the IEEE Computer Vision and Pattern Recognition, 2016, pp. 46814690.
18. A.Dash, J. C. B. Gamboa, S. Ahmed, M. Liwicki, M. Z. Afzal, Tac-gan- text conditioned auxiliary classi er generative adver sarial network,
arXiv:1703.06412 (2017).
19. Ying Liu a, Guangyu Wu b, Zhongwei Lv ,SDGAN: A novel spatial deformable generative adversarial network for low-dose CT image
reconstruction , in Science Direct 102405