[1] OODFELLOW I J, POUGET-ABADIE J, MIRZA M et al. Generative adversarial nets[C], 2672-2680(2014).

[2] LIU G Q, LIU J F, ZHU D H. Image super resolution reconstruction algorithm based on generative countermeasure network[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1720-1727(2021).

[3] LIU L Q. Image recognition algorithms based on deep convolution generative adversarial network[J]. Chinese Journal of Liquid Crystals and Displays, 35, 383-388(2020).

[4] REED S, AKATA Z, YAN X C et al. Generative adversarial text to image synthesis[C], 1060-1069(2016).

[5] ZHANG H, XU T, LI H S et al. StackGAN++: realistic image synthesis with stacked generative adversarial networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 1947-1962(2019).

[6] XU T, ZHANG P C, HUANG Q Y et al. AttnGAN: fine-grained text to image generation with attentional generative adversarial networks[C], 1316-1324(2018).

[7] JU S B, XU J, LI Y F. Text-to-single image method based on self-attention[J]. Computer Engineering and Applications, 58, 249-258(2022).

[8] LI Y H, YAO T, PAN Y W et al. Contextual transformer networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 1489-1500(2023).

[9] KINGMA D P, WELLING M. Auto-encoding variational bayes[C](2014).

[10] GREGOR K, DANIHELKA I, GRAVES A et al. DRAW: a recurrent neural network for image generation[C], 1462-1471(2015).

[11] REED S, AKATA Z, MOHAN S et al. Learning what and where to draw[C], 217-225(2016).

[12] ZHANG H, XU T, LI H S et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks[C], 5908-5916(2017).

[13] ZHU M F, PAN P B, CHEN W et al. DM-GAN: dynamic memory generative adversarial networks for text-to-image synthesis[C], 5795-5803(2019).

[14] ZHANG H, GOODFELLOW I J, METAXAS D N et al. Self-attention generative adversarial networks[C], 7354-7363(2019).

[15] SZEGEDY C, VANHOUCKE V, IOFFE S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[16] SCHUSTER M, PALIWAL K K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 45, 2673-2681(1997).

[17] DAI Y M, GIESEKE F, OEHMCKE S et al. Attentional feature fusion[C], 3559-3568(2021).

[18] WAH C, BRANSON S, WELINDER P et al. The caltech-UCSD birds-200-2011 dataset[R](2011).

[19] LIN T Y, MAIRE M, BELONGIE S et al. Microsoft COCO: common objects in context[C](2014).

[20] SALIMANS T, GOODFELLOW I, ZAREMBA W et al. Improved techniques for training GANs[C], 2234-2242(2016).

[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], 6626-6637(2017).

[22] ZHANG Z Z, XIE Y P, YANG L. Photographic text-to-image synthesis with a hierarchically-nested adversarial network[C], 6199-6208(2018).

[23] CAI Y L, WANG X R, YU Z H et al. Dualattn-GAN: text to image synthesis with dual attentional generative adversarial network[J]. IEEE Access, 7, 183706-183716(2019).

[24] RUAN S L, ZHANG Y, ZHANG K et al. DAE-GAN: dynamic aspect-aware GAN for text-to-image synthesis[C], 13940-13949(2021).

[25] TAN H C, LIU X P, LIU M et al. KT-GAN: knowledge-transfer generative adversarial network for text-to-image synthesis[J]. IEEE Transactions on Image Processing, 30, 1275-1290(2020).

[26] TAN H C, LIU X P, YIN B C et al. Cross-modal semantic matching generative adversarial networks for text-to-image synthesis[J]. IEEE Transactions on Multimedia, 24, 832-845(2022).