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The Diffusion Model (DM) has emerged as the SOTA approach for image synthesis.
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2019
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J. Cai, H. Zeng, H. Yong, Z. Cao, and L. Zhang, “Toward real-world single image super-resolution: A new benchmark and a new model,” in ICCV , 2019
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B. Xia, Y. Zhang, Y. Wang, Y. Tian, W. Yang, R. Timofte, and L. Van Gool, “Knowledge distillation based degradation estimation for blind super-resolution,” ICLR , 2023
2023
Closest in time.
Q. Wan, Z. Huang, J. Lu, G. Yu, and L. Zhang, “Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation,” ICLR , 2023
2023
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B. Xia, Y. Zhang, S. Wang, Y. Wang, X. Wu, Y. Tian, W. Yang, and L. Van Gool, “Diffir: Efficient diffusion model for image restoration,” ICCV , 2023
2023
Closest in time.
Y. Ji, Z. Chen, E. Xie, L. Hong, X. Liu, Z. Liu, T. Lu, Z. Li, and P. Luo, “Ddp: Diffusion model for dense visual prediction,” ICCV , 2023
2023
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C. Liu, S. Kumar, S. Gu, R. Timofte, and L. Van Gool, “Va-depthnet: A variational approach to single image depth prediction,” ICLR , 2023
2023
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2023
Closest in time.
J. Li, A. Hassani, S. Walton, and H. Shi, “Convmlp: Hierarchical convolutional mlps for vision,” in CVPR , 2023
2023
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