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In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model.
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Thomas Minka · 2000
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Estimating the intrinsic dimension of data with a fractal-based method
F. Camastra and A. Vinciarelli · 2002
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Stochastic neighbor embedding
Geoffrey E Hinton and Sam Roweis · 2002
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Intrinsic dimension estimation using packing numbers
Balázs Kégl · 2002
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Maximum likelihood estimation of intrinsic dimension
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Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2013
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Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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intrinsicdimension: Intrinsic dimension estimation, 2016
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Introduction to Riemannian Manifolds
J.M. Lee · 2019
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Intrinsic dimension estimation of data by principal component analysis, 2010
Mingyu Fan, Nannan Gu, Hong Qiao, and Bo Zhang · 2050
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