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IEEE Conference on Computer Vision and Pattern Recognition, pp.690-698, 2017. 10.1109\/cvpr.2017.180","DOI":"10.1109\/CVPR.2017.180"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] C. Ma, Z. Jiang, Y. Rao, J. Lu, and J. Zhou, \u201cDeep face super-resolution with iterative collaboration between attentive recovery and landmark estimation,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.5569-5578, 2020. 10.1109\/cvpr42600.2020.00561","DOI":"10.1109\/CVPR42600.2020.00561"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] X. Wang, Y. Li, H. Zhang, and Y. Shan, \u201cTowards real-world blind face restoration with generative facial prior,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.9168-9178, 2021. 10.1109\/cvpr46437.2021.00905","DOI":"10.1109\/CVPR46437.2021.00905"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] T. Karras, S. Laine, and T. 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