[1] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarialnets[C]//Proceedings of the 201427th International Conference on Neural Information Processing Systems. Cambridge:MIT Press,2014:2672-2680. [2] REED S E,AKATA Z,YAN X,et al. Generative adversarial text to image synthesis[C]//Proceedings of the 201633rd International Conference on Machine Learning. New York:ACM,2016:1060-1069. [3] HUANG H,YU P S,WANG C. An introduction to image synthesis with generative adversarialnets[EB/OL].[2020-04-22]. https://arxiv.org/pdf/1803.04469.pdf. [4] MIRZA M,OSINDERO S. Conditional generative adversarialnets[EB/OL].[2020-04-22]. https://arxiv.org/pdf/1411.1784.pdf. [5] ZHANG H,XU T,LI H,et al. StackGAN:text to photo-realistic image synthesis with stacked generative adversarialnetworks[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE,2017:5908-5916. [6] ZHANG Z,XIE Y,YANG L. Photographic text-to-image synthesis with a hierarchically-nested adversarialnetwork[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2018:6199-6208. [7] 孙钰, 李林燕, 叶子寒, 等. 多层次结构生成对抗网络的文本生成图像方法[J]. 计算机应用, 2019, 39(11):3204-3209.(SUN Y, LI L Y,YE Z H,et al. Text-to-image synthesis method based on multi-level structure generative adversarialnetworks[J]. Journal of Computer Applications,2019,39(11):3204-3209.) [8] 黄宏宇, 谷子丰. 一种基于自注意力机制的文本图像生成对抗网络[J]. 重庆大学学报, 2020, 43(3):55-61.(HUANG H Y,GU Z F. A generative adversarialnetwork base on self-attention mechanism for text-to-image generation[J]. Journal of Chongqing University,2020,43(3):55-61.) [9] XU T,ZHANG P,HUANG Q,et al. AttnGAN:fine-grained text to image generation with attentional generative adversarialnetworks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2018:1316-1324. [10] YANG Z,YUAN Y,WU Y,et al. Reviewnetworks for caption generation[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2016:2361-2369. [11] QIAO T,ZHANG J,XU D,et al. MirrorGAN:learning text-toimage generation by redescription[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:1505-1514. [12] 莫建文, 徐凯亮, 林乐平, 等. 结合互信息最大化的文本到图像生成方法[J]. 西安电子科技大学学报, 2019, 46(5):180-188. (MO J W,XU K L,LIN L P,et al. Text-to-image generationcombined with mutual information maximization[J]. Journal of Xidian University,2019,46(5):180-188.) [13] SZEGEDY C,VANHOUCKE V,IOFFE S,et al. Rethinking the inception architecture forcomputer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:2818-2826. [14] KARRAS T, LAINE S, AILA T. A style-based generator architecture for generative adversarialnetworks[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:4396-4405. [15] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2017:6000-6010. [16] LI Z,YANG J,LIU Z,et al. Feedbacknetwork for image superresolution[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2019:3862-3871. [17] SHI W,CABALLERO J,HUSZÁR F,et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neuralnetwork[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:1874-1883. [18] WAH C,BRANSON S,WELINDER P,et al. The Caltech-UCSD Birds-200-2011 dataset:CNS-TR-2011-001[R]. Pasadena:California Institute of Technology,2011. [19] NILSBACK M E,ZISSERMAN A. Automated flower classification over a large number of classes[C]//Proceedings of the 20086th Indian Conference on Computer Vision, Graphics and Image Processing. Piscataway:IEEE,2008:722-729. [20] SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[C]//Proceedings of the 201630th International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2016:2234-2242 [21] HEUSEL M,RAMSAUER H,UNTERTHINER T,et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium[C]//Proceedings of the 201731st International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2017:6626-6637. |