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Generative Adversarial Networks (GANs) have been very successful for synthesizing the images in a given dataset.
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in
2018
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2018
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2019
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2019
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2019
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2019
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2020
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2020
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2020
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W. Yu, B. Lei, M. K. Ng, A. C. Cheung, Y. Shen, and S. Wang, “Tensorizing gan with high-order pooling for alzheimer’s disease assessment,”
2021
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D. Saxena and J. Cao, “Generative adversarial networks (gans) challenges, solutions, and future directions,”
2021
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2021
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2021
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2021
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2021
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2021
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R. Zhou, C. Jiang, and Q. Xu, “A survey on generative adversarial network-based text-to-image synthesis,”
2021
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2021
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2021
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F. A. Acheampong, H. Nunoo-Mensah, and W. Chen, “Transformer models for text-based emotion detection: a review of bert-based approaches,”
2021
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K. S. Kalyan, A. Rajasekharan, and S. Sangeetha, “Ammu: a survey of transformer-based biomedical pretrained language models,”
2021
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Y. Jiang, S. Chang, and Z. Wang, “Transgan: Two pure transformers can make one strong gan, and that can scale up,”
2021
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S. Naveen, M. S. R. Kiran, M. Indupriya, T. Manikanta, and P. Sudeep, “Transformer models for enhancing attngan based text to image generation,”
2021
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2021
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D. Arad Hudson and L. Zitnick, “Compositional transformers for scene generation,”
2021
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L. Zhao, Z. Zhang, T. Chen, D. Metaxas, and H. Zhang, “Improved transformer for high-resolution gans,”
2021
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Y. Zeng, H. Yang, H. Chao, J. Wang, and J. Fu, “Improving visual quality of image synthesis by a token-based generator with transformers,”
2021
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P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in
2021
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2021
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R. Durall, S. Frolov, J. Hees, F. Raue, F.-J. Pfreundt, A. Dengel, and J. Keuper, “Combining transformer generators with convolutional discriminators,” in
2021
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Y. Korkmaz, M. Yurt, S. U. H. Dar, M. Özbey, and T. Cukur, “Deep mri reconstruction with generative vision transformers,” in
2021
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J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” in
2021
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Y. Luo, Y. Wang, C. Zu, B. Zhan, X. Wu, J. Zhou, D. Shen, and L. Zhou, “3d transformer-gan for high-quality pet reconstruction,” in
2021
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Z. Wan, J. Zhang, D. Chen, and J. Liao, “High-fidelity pluralistic image completion with transformers,” in
2021
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Y. Yu, F. Zhan, R. Wu, J. Pan, K. Cui, S. Lu, F. Ma, X. Xie, and C. Miao, “Diverse image inpainting with bidirectional and autoregressive transformers,” in
2021
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O. Dalmaz, M. Yurt, and T. Çukur, “Resvit: Residual vision transformers for multimodal medical image synthesis,”
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2022
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T. Wang, T. Zhang, B. Zhang, H. Ouyang, D. Chen, Q. Chen, and F. Wen, “Pretraining is all you need for image-to-image translation,”
2022
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2022
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2022
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S. A. Kamran, K. F. Hossain, A. Tavakkoli, S. L. Zuckerbrod, and S. A. Baker, “Vtgan: Semi-supervised retinal image synthesis and disease prediction using vision transformers,” in
2021
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L. Wang, S. Zhang, L. Gu, J. Zhang, X. Zhai, X. Sha, and S. Chang, “Automatic consecutive context perceived transformer gan for serial sectioning image blind inpainting,”
2021
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2021
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R. Liu, H. Deng, Y. Huang, X. Shi, L. Lu, W. Sun, X. Wang, J. Dai, and H. Li, “Fuseformer: Fusing fine-grained information in transformers for video inpainting,” in
2021
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X. Feng, D. Song, Y. Chen, Z. Chen, J. Ni, and H. Chen, “Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection,” in
2021
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2021
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H. Tan, X. Liu, B. Yin, and X. Li, “Dr-gan: Distribution regularization for text-to-image generation,”
2022
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Y. Korkmaz, M. Özbey, and T. Cukur, “Mri reconstruction with conditional adversarial transformers,” in
2022
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T. Wang, C. Li, J. Qiao, X. Wei, Z. Tang, and Y. Tian, “High-fidelity pluralistic image completion with plsa-vqgan,” in
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