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Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data.
Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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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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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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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Semantic image synthesis via adversarial learning
H. Dong, Simiao Yu, Chao Wu, and Y. Guo · 2017
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Deep feature interpolation for image content changes
Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Weinberger · 2017
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High-resolution image synthesis and semantic manipulation with conditional gans, 2017
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2017
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Stackgan++: Realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N. Metaxas · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N. Metaxas · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2018
Cited alongside, same era.
Deepfakes: a new threat to face recognition? assessment and detection
Pavel Korshunov and Sébastien Marcel · 2018
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Text-adaptive generative adversarial networks: Manipulating images with natural language
Seonghyeon Nam, Yunji Kim, and S. Kim · 2018
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Image2stylegan: How to embed images into the stylegan latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, J. Guo, and S. Zafeiriou · 2019
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Controlling generative models with continuous factors of variations
Antoine Plumerault, Hervé Le Borgne, and Céline Hudelot · 2020
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Encoding in style: a stylegan encoder for image-to-image translation
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Y. Azar, Stav Shapiro, and D. Cohen-Or · 2020
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Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
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Closed-form factorization of latent semantics in gans
Yujun Shen and Bolei Zhou · 2020
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Learned image downscaling for upscaling using content adaptive resampler
Wanjie Sun and Zhenzhong Chen · 2020
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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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On the” steerability” of generative adversarial networks
Ali Jahanian, Lucy Chai, and Phillip Isola · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
Cited alongside, same era.
Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, S. Laine, J. Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Manigan: Text-guided image manipulation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, and Philip H. S. Torr · 2020
Cited alongside, same era.
Later among the works it cites.
Unsupervised discovery of interpretable directions in the gan latent space
Andrey Voynov and Artem Babenko · 2020
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Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2020
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Tedigan: Text-guided diverse image generation and manipulation
Weihao Xia, Yujiu Yang, Jing-Hao Xue, and Baoyuan Wu · 2020
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David Bau, Alex Andonian, Audrey Cui, YeonHwan Park, Ali Jahanian, Aude Oliva, and Antonio Torralba · 2021
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Styleclip: Text-driven manipulation of stylegan imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, Chris Hallacy, Aditya Ramesh, G. Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, J. Clark, G. Krüger, and Ilya Sutskever · 2021
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Designing an encoder for stylegan image manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and D. Cohen-Or · 2021
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Latentclr: A contrastive learning approach for unsupervised discovery of interpretable directions
Oğuz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er, and Pinar Yanardag · 2021
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