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Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing.
2006
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Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19 . Springer, 2016, pp. 424–432
2016
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Y. Jing, Y. Liu, Y. Yang, Z. Feng, Y. Yu, D. Tao, and M. Song, “Stroke controllable fast style transfer with adaptive receptive fields,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 238–254
2018
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2020
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Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., 2020
2020
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T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” in Proc. CVPR , 2020
2020
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C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
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Y. Jing, X. Liu, Y. Ding, X. Wang, E. Ding, M. Song, and S. Wen, “Dynamic instance normalization for arbitrary style transfer,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 4369–4376
2020
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Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=PxTIG12RRHS
2021
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P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 8780–8794, 2021
2021
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D. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational diffusion models,” Advances in neural information processing systems , vol. 34, pp. 21 696–21 707, 2021
2021
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2021
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in ICML . PMLR, 2021, pp. 8821–8831
2021
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2021
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2021
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A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, “Vivit: A video vision transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 6836–6846
2021
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G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?” in ICML , vol. 2, no. 3, 2021, p. 4
2021
Cited alongside, same era.
H. Ling, K. Kreis, D. Li, S. W. Kim, A. Torralba, and S. Fidler, “Editgan: High-precision semantic image editing,” Advances in Neural Information Processing Systems , vol. 34, pp. 16 331–16 345, 2021
2021
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Y. Alaluf, O. Patashnik, and D. Cohen-Or, “Restyle: A residual-based stylegan encoder via iterative refinement,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6711–6720
2021
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O. Tov, Y. Alaluf, Y. Nitzan, O. Patashnik, and D. Cohen-Or, “Designing an encoder for stylegan image manipulation,” ACM Transactions on Graphics (TOG) , vol. 40, no. 4, pp. 1–14, 2021
2021
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2022
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2022
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2022
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2022
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E. Richardson, Y. Alaluf, O. Patashnik, Y. Nitzan, Y. Azar, S. Shapiro, and D. Cohen-Or, “Encoding in style: a stylegan encoder for image-to-image translation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 2287–2296
2021
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2021
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2022
Cited alongside, same era.
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Jing, Y. Mao, Y. Yang, Y. Zhan, M. Song, X. Wang, and D. Tao, “Learning graph neural networks for image style transfer,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII . Springer, 2022, pp. 111–128
2022
Later among the works it cites.
2022
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2022
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J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
2022
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2023
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
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