Fetching the paper…
Reading the bibliography…
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.
Reverse-Time Diffusion Equation Models
B. D. Anderson · 1982
Earlier work this paper cites.
A Survey of Numerical Methods for Stochastic Differential Equations
P. E. Kloeden and E. Platen · 1989
Earlier work this paper cites.
The Infinite Gaussian Mixture Model
C. Rasmussen · 1999
Earlier work this paper cites.
Optimal transport: old and new
C. Villani · 2009
Earlier work this paper cites.
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
D. Görür and C. Edward Rasmussen · 2010
Earlier work this paper cites.
Generative Adversarial Nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
Improved Variational Inference with Inverse Autoregressive Flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
Earlier work this paper cites.
A Note on the Evaluation of Generative Models
L. Theis, A. van den Oord, and M. Bethge · 2016
Earlier work this paper cites.
Neural Ordinary Differential Equations
R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Earlier work this paper cites.
Glow: Generative Flow with Invertible 1x1 Convolutions
D. P. Kingma and P. Dhariwal · 2018
Earlier work this paper cites.
Scalable Reversible Generative Models with Free-form Continuous Dynamics
W. Grathwohl, R. T. Q. Chen, J. Bettencourt, and D. Duvenaud · 2019
Earlier work this paper cites.
Generative Modeling by Estimating Gradients of the Data Distribution
Y. Song and S. Ermon · 2019
Cited alongside, same era.
Applied Stochastic Differential Equations
S. Särkkä and A. Solin · 2019
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. van den Berg · 2021
Cited alongside, same era.
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
V. De Bortoli, J. Thornton, J. Heng, and A. Doucet · 2021
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
Score-Based Generative Modeling through Stochastic Differential Equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Later among the works it cites.
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Y. Tashiro, J. Song, Y. Song, and S. Ermon · 2021
Later among the works it cites.
Model selection for bayesian autoencoders
B.-H. Tran, S. Rossi, D. Milios, P. Michiardi, E. V. Bonilla, and M. Filippone · 2021
Later among the works it cites.
Score-based Generative Modeling in Latent Space
A. Vahdat, K. Kreis, and J. Kautz · 2021
Later among the works it cites.
Learning to Efficiently Sample from Diffusion Probabilistic Models
D. Watson, J. Ho, M. Norouzi, and W. Chan · 2021
Later among the works it cites.
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
C.-W. Huang, J. H. Lim, and A. C. Courville · 2021
Cited alongside, same era.
Gotta Go Fast When Generating Data with Score-Based Models
A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas · 2021
Cited alongside, same era.
Variational Diffusion Models
D. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
Cited alongside, same era.
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro · 2021
Cited alongside, same era.
Improved Denoising Diffusion Probabilistic Models
A. Q. Nichol and P. Dhariwal · 2021
Cited alongside, same era.
Denoising Diffusion Implicit Models
J. Song, C. Meng, and S. Ermon · 2021
Cited alongside, same era.
T. Dockhorn, A. Vahdat, and K. Kreis · 2022
Closest in time.
Autoregressive Diffusion Models
E. Hoogeboom, A. A. Gritsenko, J. Bastings, B. Poole, R. van den Berg, and T. Salimans · 2022
Closest in time.
The Role of ImageNet Classes in Fréchet Inception Distance
T. Kynkäänniemi, T. Karras, M. Aittala, T. Aila, and J. Lehtinen · 2022
Closest in time.
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
S.-g. Lee, H. Kim, C. Shin, X. Tan, C. Liu, Q. Meng, T. Qin, W. Chen, S. Yoon, and T.-Y. Liu · 2022
Closest in time.
Progressive Distillation for Fast Sampling of Diffusion Models
T. Salimans and J. Ho · 2022
Closest in time.
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Z. Xiao, K. Kreis, and A. Vahdat · 2022
Closest in time.
Truncated diffusion probabilistic models
H. Zheng, P. He, W. Chen, and M. Zhou · 2022
Closest in time.