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Performance and generalization ability are two important aspects to evaluate the deep learning models.
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2017
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2017
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Y. Yuan, S. Liu, J. Zhang, Y. Zhang, C. Dong, and L. Lin, “Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 701–710
2018
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A. Shocher, N. Cohen, and M. Irani, ““zero-shot” super-resolution using deep internal learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3118–3126
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R. Novak, Y. Bahri, D. A. Abolafia, J. Pennington, and J. Sohl-Dickstein, “Sensitivity and generalization in neural networks: an empirical study,” 2018
2018
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Y. Wang, Y. Cao, Z.-J. Zha, J. Zhang, and Z. Xiong, “Deep degradation prior for low-quality image classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 049–11 058
2020
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2021
Later among the works it cites.
2021
Later among the works it cites.
X. Wang, Y. Li, H. Zhang, and Y. Shan, “Towards real-world blind face restoration with generative facial prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9168–9178
2021
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T. Yang, P. Ren, X. Xie, and L. Zhang, “Gan prior embedded network for blind face restoration in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 672–681
2021
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2021
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C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Communications of the ACM , vol. 64, no. 3, pp. 107–115, 2021
2021
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2021
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J. Gu, H. Cai, C. Dong, J. S. Ren, Y. Qiao, S. Gu, and R. Timofte, “Ntire 2021 challenge on perceptual image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 677–690
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 Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1833–1844
2021
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L. Wang, Y. Wang, X. Dong, Q. Xu, J. Yang, W. An, and Y. Guo, “Unsupervised degradation representation learning for blind super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 581–10 590
2021
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S. Nah, S. Baik, S. Hong, G. Moon, S. Son, R. Timofte, and K. Mu Lee, “Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 0–0
2023
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