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Most existing theoretical investigations of the accuracy of diffusion models, albeit significant, assume the score function has been approximated to a certain accuracy, and then use this a priori bound to control the error of generation.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Concise formulas for the surface area of the intersection of two hyperspherical caps
Yongjae Lee and Woo Chang Kim · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Neural temporal-difference learning converges to global optima
Qi Cai, Zhuoran Yang, Jason D Lee, and Zhaoran Wang · 2019
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Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Quadratic suffices for over-parametrization via matrix chernoff bound
Zhao Song and Xin Yang · 2019
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An improved analysis of training over-parameterized deep neural networks
Difan Zou and Quanquan Gu · 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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Gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 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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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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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 · 2021
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Adversarial purification with score-based generative models
Jongmin Yoon, Sung Ju Hwang, and Juho Lee · 2021
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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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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2022
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Score-based diffusion models for accelerated mri
Analyzing and improving the training dynamics of diffusion models
Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine · 2023
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Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
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Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Learning mixtures of gaussians using the ddpm objective
Kulin Shah, Sitan Chen, and Adam Klivans · 2023
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Hyungjin Chung and Jong Chul Ye · 2022
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Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
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Elucidating the design space of diffusion-based generative models
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Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
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Diffusion-lm improves controllable text generation
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Provable convergence of nesterov’s accelerated gradient method for over-parameterized neural networks
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SDEdit: Guided image synthesis and editing with stochastic differential equations
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De novo design of protein structure and function with rfdiffusion
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Medsegdiff: Medical image segmentation with diffusion probabilistic model
Junde Wu, RAO FU, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, and Yanwu Xu · 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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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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A survey on generative diffusion models
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An overview of diffusion models: Applications, guided generation, statistical rates and optimization
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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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Ye He, Kevin Rojas, and Molei Tao · 2024
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Optimal score estimation via empirical bayes smoothing
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The convergence of variance exploding diffusion models under the manifold hypothesis
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Quantum state generation with structure-preserving diffusion model
Yuchen Zhu, Tianrong Chen, Evangelos A Theodorou, Xie Chen, and Molei Tao · 2024
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