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Diffusion probabilistic models (DPMs) represent a class of powerful generative models.
A family of algorithms for approximate Bayesian inference
Thomas Peter Minka · 2001
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2006
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Bayesian gaussian process classification with the em-ep algorithm
Hyun-Chul Kim and Zoubin Ghahramani · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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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
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Expectation propagation for approximate bayesian inference
Thomas P Minka · 2013
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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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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 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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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Implicit generation and generalization in energy-based models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Bilateral denoising diffusion models
Max WY Lam, Jun Wang, Rongjie Huang, Dan Su, and Dong Yu · 2021
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Srdiff: Single image super-resolution with diffusion probabilistic models
Haoying Li, Yifan Yang, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen · 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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Yilun Du and Igor Mordatch · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Logan: Latent optimisation for generative adversarial networks
Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, and Timothy Lillicrap · 2019
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Vaebm: A symbiosis between variational autoencoders and energy-based models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2020
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Alex 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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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf · 2021
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Noise estimation for generative diffusion models
Robin San-Roman, Eliya Nachmani, and Lior Wolf · 2021
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Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models
Hiroshi Sasaki, Chris G Willcocks, and Toby P Breckon · 2021
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D2c: Diffusion-denoising models for few-shot conditional generation
Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon · 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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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Learning to efficiently sample from diffusion probabilistic models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
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