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Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images.
Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
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Learning fair representations
Richard S. Zemel, Ledell Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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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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Computational fairness: Preventing machine-learned discrimination
Michael Feldman · 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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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
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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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Learning non-discriminatory predictors
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 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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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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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2018
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Discriminator rejection sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, and Augustus Odena · 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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Bias correction of learned generative models using likelihood-free importance weighting
Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, and Stefano Ermon · 2019
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On the” steerability” of generative adversarial networks
Ali Jahanian, Lucy Chai, and Phillip Isola · 2019
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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Improving the fairness of deep generative models without retraining
Shuhan Tan, Yujun Shen, and Bolei Zhou · 2020
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Learning disconnected manifolds: a no gans land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, and Jérémie Mary · 2020
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Single image deraining: A comprehensive benchmark analysis, 2019
Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K. Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, and Xiaochun Cao · 2019
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Characterizing bias in classifiers using generative models
Daniel McDuff, Shuang Ma, Yale Song, and Ashish Kapoor · 2019
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Discriminator optimal transport
A. Tanaka · 2019
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Spatial attentive single-image deraining with a high quality real rain dataset, 2019
Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson Lau · 2019
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Rewriting a deep generative model
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, and Antonio Torralba · 2020
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Fair generative modeling via weak supervision
Aditya Grover, Kristy Choi, Rui Shu, and Stefano Ermon · 2020
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Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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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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Rameen Abdal, Peihao Zhu, Niloy Jyoti Mitra, and Peter Wonka · 2021
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Stylemc: Multi-channel based fast text-guided image generation and manipulation
Umut Kocasari, Alara Dirik, Mert Tiftikci, and Pinar Yanardag · 2021
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Explaining in style: Training a gan to explain a classifier in stylespace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, and Inbar Mosseri · 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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Fair attribute classification through latent space de-biasing
Vikram V. Ramaswamy, Sunnis S. Y. Kim, and Olga Russakovsky · 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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