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Traditionally, the main focus of image super-resolution techniques is on recovering the most likely high-quality images from low-quality images, using a one-to-one low- to high-resolution mapping.
“Generative adversarial nets,”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, · 2014
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 2015
Earlier work this paper cites.
“Deep learning face attributes in the wild,”
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang, · 2015
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2015
Earlier work this paper cites.
“Perceptual losses for real-time style transfer and super-resolution,”
Justin Johnson, Alexandre Alahi, and Fei-Fei Li, · 2016
Earlier work this paper cites.
“Improved techniques for training gans,”
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Earlier work this paper cites.
“Image-to-image translation with conditional adversarial networks,”
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros, · 2016
Earlier work this paper cites.
“Photo-realistic single image super-resolution using a generative adversarial network,”
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi, · 2016
Cited alongside, same era.
“Towards Principled Methods for Training Generative Adversarial Networks,”
Martin Arjovsky and Léon Bottou, · 2017
Cited alongside, same era.
“Unpaired image-to-image translation using cycle-consistent adversarial networks,”
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros, · 2017
Cited alongside, same era.
“Gans trained by a two time-scale update rule converge to a nash equilibrium,”
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter, · 2017
Cited alongside, same era.
“Explorable Super Resolution,”
Yuval Bahat and Tomer Michaeli, · 2019
Later among the works it cites.
“Diversity-sensitive conditional generative adversarial networks,”
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee, · 2019
Later among the works it cites.
“MSG-GAN: multi-scale gradient GAN for stable image synthesis,”
Animesh Karnewar, Oliver Wang, and Raghu Sesha Iyengar, · 2019
Later among the works it cites.
“Deepsee: Deep disentangled semantic explorative extreme super-resolution,”
Marcel Christoph Bühler, A. Romero, and R. Timofte, · 2020
Later among the works it cites.
“PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models,”
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Lars M. Mescheder, · 2018
Cited alongside, same era.
“ESRGAN: enhanced super-resolution generative adversarial networks,”
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang, · 2018
Cited alongside, same era.
“The unreasonable effectiveness of deep features as a perceptual metric,”
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang, · 2018
Cited alongside, same era.
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin, · 2020
Later among the works it cites.
“Structure-Preserving Super Resolution with Gradient Guidance,”
Cheng Ma, Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu, and Jie Zhou, · 2020
Later among the works it cites.
“Creating High Resolution Images with a Latent Adversarial Generator,”
David Berthelot, Peyman Milanfar, and Ian Goodfellow, · 2020
Later among the works it cites.