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Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-the-art performance across various domains.
Beitrag zur näherungsweisen Integration totaler Differentialgleichungen
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High strong order explicit runge-kutta methods for stochastic ordinary differential equations
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Stochastic differential equations
Bernt Øksendal · 2003
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Exponential integrators
Marlis Hochbruck and Alexander Ostermann · 2010
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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
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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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Auto-encoding variational bayes
Diederik P Kingma · 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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Brownian motion, martingales, and stochastic calculus
Jean-François Le Gall · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Generative modeling with denoising auto-encoders and langevin sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Information theory methods in statistics and computer science
Yury Polyanskiy · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Grad-tts: A diffusion probabilistic model for text-to-speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
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Noise estimation for generative diffusion models
Robin San-Roman, Eliya Nachmani, and Lior Wolf · 2021
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
Cited alongside, same era.
On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, et al · 2023
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Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2023
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Diffusion-based time series imputation and forecasting with structured state space models
Juan Miguel Lopez Alcaraz and Nils Strodthoff · 2022
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Guided-tts 2: A diffusion model for high-quality adaptive text-to-speech with untranscribed data
Sungwon Kim, Heeseung Kim, and Sungroh Yoon · 2022
Cited alongside, same era.
Statistical efficiency of score matching: The view from isoperimetry
Frederic Koehler, Alexander Heckett, and Andrej Risteski · 2022
Cited alongside, same era.
Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2023
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Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2023
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Nearly d-linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2024
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What does guidance do? a fine-grained analysis in a simple setting
Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, and Jianfeng Lu · 2024
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From optimal score matching to optimal sampling
Zehao Dou, Subhodh Kotekal, Zhehao Xu, and Harrison H Zhou · 2024
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Optimal convex m m -estimation via score matching
Oliver Y Feng, Yu-Chun Kao, Min Xu, and Richard J Samworth · 2024
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Convergence analysis for general probability flow odes of diffusion models in wasserstein distances
Xuefeng Gao and Lingjiong Zhu · 2024
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Seeds: Exponential sde solvers for fast high-quality sampling from diffusion models
Martin Gonzalez, Nelson Fernandez Pinto, Thuy Tran, Hatem Hajri, Nader Masmoudi, et al · 2024
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Faster diffusion-based sampling with randomized midpoints: Sequential and parallel
Shivam Gupta, Linda Cai, and Sitan Chen · 2024
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Neural network-based score estimation in diffusion models: Optimization and generalization
Yinbin Han, Meisam Razaviyayn, and Renyuan Xu · 2024
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Convergence analysis of probability flow ode for score-based generative models
Daniel Zhengyu Huang, Jiaoyang Huang, and Zhengjiang Lin · 2024
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Non-asymptotic convergence of discrete-time diffusion models: New approach and improved rate
Yuchen Liang, Peizhong Ju, Yingbin Liang, and Ness Shroff · 2024
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Adaptivity of diffusion models to manifold structures
Rong Tang and Yun Yang · 2024
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Score-based diffusion models via stochastic differential equations–a technical tutorial
Wenpin Tang and Hanyang Zhao · 2024
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Optimal score estimation via empirical bayes smoothing
Andre Wibisono, Yihong Wu, and Kaylee Yingxi Yang · 2024
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Theoretical insights for diffusion guidance: A case study for gaussian mixture models
Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei · 2024
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Sa-solver: Stochastic adams solver for fast sampling of diffusion models
Shuchen Xue, Mingyang Yi, Weijian Luo, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhi-Ming Ma · 2024
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Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions
Kaihong Zhang, Heqi Yin, Feng Liang, and Jingbo Liu · 2024
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Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu · 2024
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