Fetching the paper…
Reading the bibliography…
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless some sophisticated sampling strategies are employed.
Stochastic integral
Kiyosi Itô · 1944
Earlier work this paper cites.
Continuous Markov processes and stochastic equations
Gisirō Maruyama · 1955
Earlier work this paper cites.
The theory of stochastic processes
David Roxbee Cox and Hilton David Miller · 1965
Earlier work this paper cites.
Conditional Markov processes
Ruslan Leont’evich Stratonovich · 1965
Earlier work this paper cites.
Brownian dynamics as smart Monte Carlo simulation
Peter J Rossky, Jimmie D Doll, and Harold L Friedman · 1978
Earlier work this paper cites.
A second course in stochastic processes
Samuel Karlin and Howard E Taylor · 1981
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
Earlier work this paper cites.
On a Taylor formula for a class of Ito processes
Eckhard Platen and Wolfgang Wagner · 1982
Earlier work this paper cites.
Numerical solution of stochastic differential equations
Peter E Kloeden and Eckhard Platen · 1992
Earlier work this paper cites.
Numerical solution of SDE through computer experiments
Peter E Kloeden, Eckhard Platen, and Henri Schurz · 1994
Earlier work this paper cites.
Exponential convergence of Langevin distributions and their discrete approximations
Gareth O Roberts and Richard L Tweedie · 1996
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
Earlier work this paper cites.
Stochastic calculus for finance II: Continuous-time models , volume 11
Steven E Shreve · 2004
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Numerical recipes 3rd edition: The art of scientific computing
William H Press, Saul A Teukolsky, William T Vetterling, and Brian P Flannery · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
MNIST handwritten digit database, 2010
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Stochastic differential equations: an introduction with applications
Bernt Øksendal · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
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
Cited alongside, same era.
SymPy: symbolic computing in Python
Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B. Kirpichev, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K. Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E. Granger, Richard P. Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J. Curry, Andy R. Terrel, Štěpán Roučka, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, and Anthony Scopatz · 2017
Score-based generative modeling with critically-damped Langevin diffusion
Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2021
Closest in time.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Closest in time.
Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2021
Closest in time.
Argmax flows and multinomial diffusion: Towards non-autoregressive language models
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Closest in time.
Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Cited alongside, same era.
An introduction to variational autoencoders
Diederik P Kingma and Max Welling · 2019
Cited alongside, same era.
Applied stochastic differential equations , volume 10 of Institute of Mathematical Statistics Textbooks
Simo Särkkä and Arno Solin · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
WaveGrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Closest in time.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Closest in time.
Symbolic music generation with diffusion models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
Closest in time.
Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
Closest in time.
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
Closest in time.
Grad-TTS: A diffusion probabilistic model for text-to-speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
Closest in time.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
Closest in time.
UNIT-DDPM: Unpaired image translation with denoising diffusion probabilistic models
Hiroshi Sasaki, Chris G Willcocks, and Toby P Breckon · 2021
Closest in time.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Closest in time.
Learning to efficiently sample from diffusion probabilistic models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
Closest in time.
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
Closest in time.
Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Closest in time.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Closest in time.
Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
Closest in time.