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We study overparameterization in generative adversarial networks (GANs) that can interpolate the training data.
Two models of double descent for weak features
M. Belkin, D. Hsu, and J. Xu · 1903
Earlier work this paper cites.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 1912
Earlier work this paper cites.
On measures of entropy and information
Alfréd Rényi et al · 1961
Earlier work this paper cites.
Principles of mathematical analysis
Walter Rudin · 1964
Earlier work this paper cites.
The distance between two random vectors with given dispersion matrices
Ingram Olkin and Friedrich Pukelsheim · 1982
Earlier work this paper cites.
A class of Wasserstein metrics for probability distributions
Clark R Givens, Rae Michael Shortt, et al · 1984
Earlier work this paper cites.
Real and complex analysis. 1987
Walter Rudin · 1987
Earlier work this paper cites.
Real analysis , volume 32
Halsey Lawrence Royden and Patrick Fitzpatrick · 1988
Earlier work this paper cites.
Linear algebra done right , volume 2
Sheldon Jay Axler · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Smooth manifolds
John M Lee · 2003
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Cédric Villani · 2003
Earlier work this paper cites.
Classification vs regression in overparameterized regimes: Does the loss function matter?
V. Muthukumar, A. Narang, V. Subramanian, M. Belkin, D. Hsu, and A. Sahai · 2005
Earlier work this paper cites.
Pattern recognition and machine learning
Christopher M Bishop · 2006
Earlier work this paper cites.
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James E Gentle · 2007
Earlier work this paper cites.
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Cédric Villani · 2008
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The elements of statistical learning: data mining, inference, and prediction
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Cited alongside, same era.
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Roger A Horn and Charles R Johnson · 2012
Cited alongside, same era.
The matrix cookbook, version 20121115
KB Petersen and MS Pedersen · 2012
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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Which training methods for GANs do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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A jamming transition from under-to over-parametrization affects loss landscape and generalization
S. Spigler, M. Geiger, S. d’Ascoli, L. Sagun, G. Biroli, and M. Wyart · 2018
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A model of double descent for high-dimensional binary linear classification
Z. Deng, A. Kammoun, and C. Thrampoulidis · 2019
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Surprises in high-dimensional ridgeless least squares interpolation
T. Hastie, A. Montanari, S. Rosset, and R. J. Tibshirani · 2019
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Measure, Integration & Real Analysis
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ImageNet Large Scale Visual Recognition Challenge
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f-GAN: Training generative neural samplers using variational divergence minimization
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Improved techniques for training GANs
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Wasserstein generative adversarial networks
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Improved training of Wasserstein GANs
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Benign overfitting in linear regression
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Subspace fitting meets regression: The effects of supervision and orthonormality constraints on double descent of generalization errors
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Double trouble in double descent: Bias and variance(s) in the lazy regime
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Understanding GANs in the LQG setting: Formulation, generalization and stability
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Analytic study of double descent in binary classification: The impact of loss
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Optimal regularization can mitigate double descent
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Understanding overparameterization in generative adversarial networks
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RGAN: Rényi generative adversarial network
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Double double descent: On generalization errors in transfer learning between linear regression tasks
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