AttnGAN: Realistic Text-to-Image Synthesis with Attentional Generative Adversarial Networks

Author:

Mathesul Shubham,Bhutkar GaneshORCID,Rambhad Ayush

Publisher

Springer International Publishing

Reference9 articles.

1. Campo, M.: Architecture, language and AI - language, attentional generative adversarial networks (AttnGAN) and architecture design. In: Proceedings of the 26th CAADRIA Conference, vol 1, Hong Kong, pp. 211–220 (2021)

2. Hashemiet, A., Mozaffari, S.: Secure deep neural networks using adversarial image generation and training with noise-GAN. Comput. Secur. 86, 372–387 (2019)

3. Tao, X., et al.: AttnGAN: fine-grained text to image generation with attentional generative adversarial networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1316–1324 (2018)

4. Minfeng, Z., Pingbo, P., Wei, C., Yi, Y.: DM-GAN: dynamic memory generative adversarial networks for text-to-image synthesis. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5795–5803 (2019)

5. Hajar, E., Majid, M., Ming, D., Chinnam, R.: SPA-GAN: spatial attention GAN for image-to-image translation. IEEE Trans. Multimed. 23, 391–401 (2020)

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