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Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Image quality metrics: Psnr vs. ssim
Alain Horé and Djemel Ziou · 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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic backpropagation and approximate inference in deep generative models, 2014
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Variational inference with normalizing flows, 2015
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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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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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Glow: Generative flow with invertible 1x1 convolutions, 2018
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Deep learning using rectified linear units (relu), 2019
Abien Fred Agarap · 2019
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Augmented neural odes, 2019
Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
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Latent odes for irregularly-sampled time series, 2019
Yulia Rubanova, Ricky T. Q. Chen, and David Duvenaud · 2019
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Generalizing variational autoencoders with hierarchical empirical bayes, 2020
Wei Cheng, Gregory Darnell, Sohini Ramachandran, and Lorin Crawford · 2020
Earlier work this paper cites.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
On second order behaviour in augmented neural odes
Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, and Pietro Liò · 2020
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution, 2020
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis, 2021
Prafulla Dhariwal and Alex Nichol · 2021
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation, 2021
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans · 2021
Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents, 2022
Kushagra Pandey, Avideep Mukherjee, Piyush Rai, and Abhishek Kumar · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Photorealistic text-to-image diffusion models with deep language understanding, 2022
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Sindiffusion: Learning a diffusion model from a single natural image, 2022
Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, and Houqiang Li · 2022
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Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis, 2021
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Score-based generative modeling in latent space, 2021
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Cited alongside, same era.
Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
Cited alongside, same era.
Improving diffusion models for inverse problems using manifold constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye · 2022
Cited alongside, same era.
Score-based generative modeling with critically-damped langevin diffusion, 2022
Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2022
Cited alongside, same era.
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion gans, 2022
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
Later among the works it cites.
Memory efficient diffusion probabilistic models via patch-based generation, 2023
Shinei Arakawa, Hideki Tsunashima, Daichi Horita, Keitaro Tanaka, and Shigeo Morishima · 2023
Closest in time.
Continuous u-net: Faster, greater and noiseless
Chun-Wun Cheng, Christina Runkel, Lihao Liu, Raymond H Chan, Carola-Bibiane Schönlieb, and Angelica I Aviles-Rivero · 2023
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Diffusion posterior sampling for general noisy inverse problems, 2023
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann, Marc L. Klasky, and Jong Chul Ye · 2023
Closest in time.
Masked diffusion transformer is a strong image synthesizer, 2023
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan · 2023
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Reflected diffusion models, 2023
Aaron Lou and Stefano Ermon · 2023
Closest in time.
A complete recipe for diffusion generative models, 2023
Kushagra Pandey and Stephan Mandt · 2023
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Solving linear inverse problems provably via posterior sampling with latent diffusion models, 2023
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alexandros G. Dimakis, and Sanjay Shakkottai · 2023
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Patch diffusion: Faster and more data-efficient training of diffusion models, 2023
Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang Wang, Weizhu Chen, and Mingyuan Zhou · 2023
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Fast sampling of diffusion models with exponential integrator, 2023
Qinsheng Zhang and Yongxin Chen · 2023
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