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Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image.
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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Foundations of Data Science
John Hopcroft and Ravi Kannan · 2014
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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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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Invertible conditional gans for image editing
Guim Perarnau, Joost Van De Weijer, Bogdan Raducanu, and Jose M Álvarez · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
Fader networks: Manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Cited alongside, same era.
Disentangled representation learning gan for pose-invariant face recognition
Luan Tran, Xi Yin, and Xiaoming Liu · 2017
Cited alongside, same era.
Deep feature interpolation for image content changes
Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Weinberger · 2017
Cited alongside, same era.
Towards large-pose face frontalization in the wild
Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, and Manmohan Chandraker · 2017
Cited alongside, same era.
Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2018
Cited alongside, same era.
Towards open-set identity preserving face synthesis
Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua · 2018
The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P Thomas Fletcher · 2018
Later among the works it cites.
Faceid-gan: Learning a symmetry three-player gan for identity-preserving face synthesis
Yujun Shen, Ping Luo, Junjie Yan, Xiaogang Wang, and Xiaoou Tang · 2018
Later among the works it cites.
Facefeat-gan: a two-stage approach for identity-preserving face synthesis
Yujun Shen, Bolei Zhou, Ping Luo, and Xiaoou Tang · 2018
Later among the works it cites.
Elegant: Exchanging latent encodings with gan for transferring multiple face attributes
Taihong Xiao, Jiapeng Hong, and Jinwen Ma · 2018
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Visualizing and understanding generative adversarial networks
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, and Antonio Torralba · 2019
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Cited alongside, same era.
Optimizing the latent space of generative networks
Piotr Bojanowski, Armand Joulin, David Lopez-Pas, and Arthur Szlam · 2018
Cited alongside, same era.
Metrics for deep generative models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick van der Smagt · 2018
Cited alongside, same era.
Semantically decomposing the latent spaces of generative adversarial networks
Chris Donahue, Akshay Balsubramani, Julian McAuley, and Zachary C. Lipton · 2018
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Latent space non-linear statistics
Line Kuhnel, Tom Fletcher, Sarang Joshi, and Stefan Sommer · 2018
Cited alongside, same era.
Feature-based metrics for exploring the latent space of generative models
Samuli Laine · 2018
Cited alongside, same era.
Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola · 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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Semantic hierarchy emerges in deep generative representations for scene synthesis
Ceyuan Yang, Yujun Shen, and Bolei Zhou · 2019
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Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
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Lia: Latently invertible autoencoder with adversarial learning
Jiapeng Zhu, Deli Zhao, and Bo Zhang · 2019
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Image processing using multi-code gan prior
Jinjin Gu, Yujun Shen, and Bolei Zhou · 2020
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On the ”steerability” of generative adversarial networks
Ali Jahanian, Lucy Chai, and Phillip Isola · 2020
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