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Deep generative models like StyleGAN hold the promise of semantic image editing: modifying images by their content, rather than their pixel values.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2014
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The loss surfaces of multilayer networks
Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Hypernetworks, 2016
David Ha, Andrew Dai, and Quoc V. Le · 2016
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Deep learning without poor local minima
Kenji Kawaguchi · 2016
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Local minima in training of neural networks
Grzegorz Swirszcz, Wojciech Marian Czarnecki, and Razvan Pascanu · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Image2stylegan: How to embed images into the stylegan latent space?, 2019
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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On the intrinsic dimensionality of image representations
Sixue Gong, Vishnu Naresh Boddeti, and Anil K. Jain · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
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Image2stylegan++: How to edit the embedded images?, 2020
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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On noise injection in generative adversarial networks
Ruili Feng, Deli Zhao, and Zhengjun Zha · 2020
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Collaborative learning for faster stylegan embedding, 2020
Shanyan Guan, Ying Tai, Bingbing Ni, Feida Zhu, Feiyue Huang, and Xiaokang Yang · 2020
Cited alongside, same era.
Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Transforming and projecting images into class-conditional generative networks, 2020
Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, and Aaron Hertzmann · 2020
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Training generative adversarial networks with limited data, 2020
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan, 2020
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Pulse: Self-supervised photo upsampling via latent space exploration of generative models, 2020
Gan inversion for out-of-range images with geometric transformations, 2021
Kyoungkook Kang, Seongtae Kim, and Sunghyun Cho · 2021
Later among the works it cites.
Alias-free generative adversarial networks, 2021
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Exploiting spatial dimensions of latent in gan for real-time image editing
Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, and Youngjung Uh · 2021
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Transforming the latent space of stylegan for real face editing
Heyi Li, Jinlong Liu, Yunzhi Bai, Huayan Wang, and Klaus Mueller · 2021
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2021
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Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
Cited alongside, same era.
Face identity disentanglement via latent space mapping
Yotam Nitzan, Amit Bermano, Yangyan Li, and Daniel Cohen-Or · 2020
Cited alongside, same era.
Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
Cited alongside, same era.
Stylerig: Rigging stylegan for 3d control over portrait images
Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt · 2020
Cited alongside, same era.
Improving inversion and generation diversity in stylegan using a gaussianized latent space, 2020
Jonas Wulff and Antonio Torralba · 2020
Cited alongside, same era.
Image gans meet differentiable rendering for inverse graphics and interpretable 3d neural rendering
Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, and Sanja Fidler · 2020
Cited alongside, same era.
In-domain gan inversion for real image editing, 2020
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
Cited alongside, same era.
Styleclip: Text-driven manipulation of stylegan imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski · 2021
Later among the works it cites.
The intrinsic dimension of images and its impact on learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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Encoding in style: a stylegan encoder for image-to-image translation, 2021
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or · 2021
Later among the works it cites.
Pivotal tuning for latent-based editing of real images
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or · 2021
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An introduction to deep generative modeling
Lars Ruthotto and Eldad Haber · 2021
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Designing an encoder for stylegan image manipulation, 2021
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or · 2021
Later among the works it cites.
A simple baseline for stylegan inversion
Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Weiming Zhang, Lu Yuan, Gang Hua, and Nenghai Yu · 2021
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Gan inversion: A survey, 2021
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
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From continuity to editability: Inverting gans with consecutive images
Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, and Shengfeng He · 2021
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Generative hierarchical features from synthesizing images
Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, and Bolei Zhou · 2021
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Improved stylegan embedding: Where are the good latents?, 2021
Peihao Zhu, Rameen Abdal, Yipeng Qin, John Femiani, and Peter Wonka · 2021
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Third time’s the charm? image and video editing with stylegan3
Yuval Alaluf, Or Patashnik, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, and Daniel Cohen-Or · 2022
Closest in time.
Ae-stylegan: Improved training of style-based auto-encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, and Dimitris Metaxas · 2022
Closest in time.
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Closest in time.