Fetching the paper…
Reading the bibliography…
Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorithm improvement and extensions to discrete, structured, non-Euclidean domains.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
Earlier work this paper cites.
Classical potential theory and its probabilistic counterpart , volume 549
Joseph L Doob and JI Doob · 1984
Earlier work this paper cites.
The ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin · 1999
Earlier work this paper cites.
Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
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 Oksendal · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
Earlier work this paper cites.
Reciprocal processes. a measure-theoretical point of view
Christian Léonard, Sylvie Rœlly, and Jean-Claude Zambrini · 2014
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
Importance weighted autoencoders
Yuri Burda, Roger B Grosse, and Ruslan Salakhutdinov · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
The girsanov theorem without (so much) stochastic analysis
Antoine Lejay · 2018
Cited alongside, same era.
Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Cited alongside, same era.
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Later among the works it cites.
A variational perspective on diffusion-based generative models and score matching
Chin-Wei Huang, Jae Hyun Lim, and Aaron C Courville · 2021
Later among the works it cites.
On fast sampling of diffusion probabilistic models
Zhifeng Kong and Wei Ping · 2021
Later among the works it cites.
Sampling with trusthworthy constraints: A variational gradient framework
Xingchao Liu, Xin Tong, and Qiang Liu · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 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.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Later among the works it cites.
Non-denoising forward-time diffusions
Stefano Peluchetti · 2021
Later among the works it cites.
Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
Later among the works it cites.
Deep generative learning via schrödinger bridge
Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, and Can Yang · 2021
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
Later among the works it cites.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Later among the works it cites.
Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 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.