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Generative adversarial networks (GANs) are so complex that the existing learning theories do not provide a satisfactory explanation for why GANs have great success in practice.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Introduction to statistical learning theory
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi · 2004
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Image augmentations for gan training
Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang · 2006
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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Robust regression and lasso
Huan Xu, Constantine Caramanis, and Shie Mannor · 2010
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Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
Haim Avron and Sivan Toledo · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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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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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Zeyuan Allen-Zhu · 2017
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Leon Bottou · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Leon Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2017
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
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Energy-based generative adversarial networks
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2017
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Many paths to equilibrium: Gans do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2018
Cited alongside, same era.
Neural tangent kernel: convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
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Lipschitz generative adversarial nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang · 2019
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Benign overfitting in linear regression
Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 2020
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Smoothness and stability in gans
Casey Chu, Kentaro Minami, and Kenji Fukumizu · 2020
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2020
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2020
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Cited alongside, same era.
Dropout training, data-dependent regularization, and generalization bounds
Wenlong Mou, Yuchen Zhou, Jun Gao, and Liwei Wang · 2018
Cited alongside, same era.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
Cited alongside, same era.
On the convergence and robustness of training gans with regularized optimal transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
Cited alongside, same era.
Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
Cited alongside, same era.
On the discrimination-generalization tradeoff in gans
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
On the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels
Tengyuan Liang, Alexander Rakhlin, and Xiyu Zhai · 2020
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On convergence and generalization of dropout training
Poorya Mianjy and Raman Arora · 2020
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In defense of uniform convergence: Generalization via derandomization with an application to interpolating predictors
Jeffrey Negrea, Gintare Karolina Dziugaite, and Daniel Roy · 2020
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Global convergence of deep networks with one wide layer followed by pyramidal topology
Quynh Nguyen and Marco Mondelli · 2020
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Loss-sensitive generative adversarial networks on lipschitz densities
Guo-Jun Qi · 2020
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Learning disconnected manifolds: a no gan’s land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, and Jeremie Mary · 2020
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Understanding and stabilizing gans’ training dynamics with control theory
Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu, and Bo Zhang · 2020
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Consistency regularization for generative adversarial networks
Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee · 2020
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Gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2020
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Dropout: Explicit forms and capacity control
Raman Arora, Peter Bartlett, Poorya Mianjy, and Nathan Srebro · 2021
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Understanding over-parameterization in generative adversarial networks
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, and Soheil Feizi · 2021
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Mathematical models of overparameterized neural networks
Cong Fang, Hanze Dong, and Tong Zhang · 2021
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Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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Regularization matters: A nonparametric perspective on overparametrized neural network
Tianyang Hu, Wenjia Wang, Cong Lin, and Guang Cheng · 2021
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Early-stopped neural networks are consistent
Ziwei Ji, Justin D Li, and Matus Telgarsky · 2021
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Nonparametric regression with shallow overparameterized neural networks trained by gd with early stopping
Ilja Kuzborskij and Csaba Szepesvári · 2021
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On the proof of global convergence of gradient descent for deep relu networks with linear widths
Quynh Nguyen · 2021
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Training robust neural networks using lipschitz bounds
Patricia Pauli, Anne Koch, Julian Berberich, Paul Kohler, and Frank Allgower · 2021
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On data augmentation for gan training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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