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Diffusion models are powerful generative models that map noise to data using stochastic processes.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich · 1908
Earlier work this paper cites.
Classical potential theory and its probabilistic counterpart , volume 262
Joseph L Doob and JI Doob · 1984
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Diffusions, Markov processes and martingales: Volume 2, Itô calculus , volume 2
L Chris G Rogers and David Williams · 2000
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Simulation of conditioned diffusion and application to parameter estimation
Bernard Delyon and Ying Hu · 2006
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Optimal transport: Old and new
Cédric Villani · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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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 · 2011
Earlier work this paper cites.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Inequivalence of nonequilibrium path ensembles: the example of stochastic bridges
Juraj Szavits-Nossan and Martin R Evans · 2015
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Guided proposals for simulating multi-dimensional diffusion bridges
Moritz Schauer, Frank Van Der Meulen, and Harry Van Zanten · 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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Shane Barratt and Rishi Sharma · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Applied stochastic differential equations , volume 10
Simo Särkkä and Arno Solin · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Dual diffusion implicit bridges for image-to-image translation
Xuan Su, Jiaming Song, Chenlin Meng, and Stefano Ermon · 2022
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2022
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Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Simulating diffusion bridges with score matching
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Improved denoising diffusion probabilistic models
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Palette: Image-to-image diffusion models
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Building normalizing flows with stochastic interpolants
Michael Samuel Albergo and Eric Vanden-Eijnden · 2023
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Inversion by direct iteration: An alternative to denoising diffusion for image restoration
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simple diffusion: End-to-end diffusion for high resolution images
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Stefano Peluchetti · 2023
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Diffusion schr \ \backslash " odinger bridge matching
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Aligned diffusion schr \ \backslash " odinger bridges
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