2020
DOI: 10.1007/978-3-030-58621-8_39
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CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing

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Cited by 14 publications
(9 citation statements)
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“…This metric is used in many papers such as [19,50,91,100,119,120,144,152,174,198,206]. There are also some other versions of SSIM index such as MSSIM (mean SSIM) and MS-SSIM (multiscale SSIM).…”
Section: B Evaluation Metricsmentioning
confidence: 99%
“…This metric is used in many papers such as [19,50,91,100,119,120,144,152,174,198,206]. There are also some other versions of SSIM index such as MSSIM (mean SSIM) and MS-SSIM (multiscale SSIM).…”
Section: B Evaluation Metricsmentioning
confidence: 99%
“…These works are limited to low resolution images. Recently, Chen et al [12] proposed a pixel translation framework for high resolution facial image editing. Viazovetskyi et al [13] used generated high resolution images to train the pix2pixHD [14] for facial attribute manipulation.…”
Section: Related Workmentioning
confidence: 99%
“…Several studies investigate different possibilities to tackle high resolution images. CooGAN [7] proposed a patch-based local-global framework to process HR images in patches. Observing the great progress of generative networks in high quality image synthesis, Viazovetskyi et al [39] trained the pix2pixHD model [40] for single attribute editing with the synthetic images generated by StyleGAN2 [21].…”
Section: Related Workmentioning
confidence: 99%