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Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images.
On the differentiability of isometries
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Natural gradient works efficiently in learning
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ARPACK users’ guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Information geometry and its applications , volume 194
Shun-ichi Amari · 2016
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Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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Tom White · 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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Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 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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Metrics for deep generative models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick Smagt · 2018
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Gradient descent aligns the layers of deep linear networks
Ziwei Ji and Matus Telgarsky · 2018
Exploiting gan internal capacity for high-quality reconstruction of natural images
Marcos Pividori, Guillermo L Grinblat, and Lucas C Uzal · 2019
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Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences
Carlos R Ponce, Will Xiao, Peter F Schade, Till S Hartmann, Gabriel Kreiman, and Margaret S Livingstone · 2019
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Human-in-the-loop differential subspace search in high-dimensional latent space
Chia-Hsing Chiu, Yuki Koyama, Yu-Chi Lai, Takeo Igarashi, and Yonghao Yue · 2020
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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
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A spectral regularizer for unsupervised disentanglement
Aditya Ramesh, Youngduck Choi, and Yann LeCun · 2018
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The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P Thomas Fletcher · 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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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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An investigation into neural net optimization via hessian eigenvalue density
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 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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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, and Aaron Hertzmann · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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The hessian penalty: A weak prior for unsupervised disentanglement
William Peebles, John Peebles, Jun-Yan Zhu, Alexei Efros, and Antonio Torralba · 2020
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Closed-form factorization of latent semantics in gans
Yujun Shen and Bolei Zhou · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 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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Xdream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization
Will Xiao and Gabriel Kreiman · 2020
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Averaging symmetric positive-definite matrices
Xinru Yuan, Wen Huang, Pierre-Antoine Absil, and Kyle A Gallivan · 2020
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