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Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables.
A family of embedded runge-kutta formulae
J. R. Dormand and P. J. Prince · 1980
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Reverse-time diffusion equation models
B. D. Anderson · 1982
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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A connection between score matching and denoising autoencoders
P. Vincent · 2011
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Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Pixel recurrent neural networks
A. Van Den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Density estimation using real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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JAX: composable transformations of Python+NumPy programs
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
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Neural ordinary differential equations
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
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Autoencoder-based network anomaly detection
Z. Chen, C. K. Yeo, B. S. Lee, and C. T. Lau · 2018
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Glow: Generative flow with invertible 1x1 convolutions
D. P. Kingma and P. Dhariwal · 2018
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Scalable reversible generative models with free-form continuous dynamics
W. Grathwohl, R. T. Q. Chen, J. Bettencourt, and D. Duvenaud · 2019
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha · 2020
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Anomaly detection in trajectory data with normalizing flows
M. L. Dias, C. L. C. Mattos, T. L. da Silva, J. A. F. de Macedo, and W. C. Silva · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Learning differential equations that are easy to solve
J. Kelly, J. Bettencourt, M. J. Johnson, and D. K. Duvenaud · 2020
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Input complexity and out-of-distribution detection with likelihood-based generative models
J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, and J. Luque · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Z. Xiao, Q. Yan, and Y. Amit · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
V. De Bortoli, J. Thornton, J. Heng, and A. Doucet · 2021
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Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
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Anfic: Image compression using augmented normalizing flows
Y.-H. Ho, C.-C. Chan, W.-H. Peng, H.-M. Hang, and M. Domański · 2021
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Variational diffusion models
D. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2021
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Maximum likelihood training of score-based diffusion models
Y. Song, C. Durkan, I. Murray, and S. Ermon · 2021
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
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Minimizing trajectory curvature of ode-based generative models
S. Lee, B. Kim, and J. C. Ye · 2023
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Flow matching for generative modeling
Y. Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023
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Flow straight and fast: Learning to generate and transfer data with rectified flow
X. Liu, C. Gong, and qiang liu · 2023
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Wavelet diffusion models are fast and scalable image generators
H. Phung, Q. Dao, and A. Tran · 2023
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Multisample flow matching: Straightening flows with minibatch couplings
A.-A. Pooladian, H. Ben-Hamu, C. Domingo-Enrich, B. Amos, Y. Lipman, and R. Chen · 2023
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Generative modelling with inverse heat dissipation
S. Rissanen, M. Heinonen, and A. Solin · 2023
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H. Tachibana, M. Go, M. Inahara, Y. Katayama, and Y. Watanabe · 2021
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Score-based generative modeling in latent space
A. Vahdat, K. Kreis, and J. Kautz · 2021
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Diffusion normalizing flow
Q. Zhang and Y. Chen · 2021
Cited alongside, same era.
Soft diffusion: Score matching for general corruptions
G. Daras, M. Delbracio, H. Talebi, A. G. Dimakis, and P. Milanfar · 2022
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Cascaded diffusion models for high fidelity image generation
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans · 2022
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Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
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On kinetic optimal probability paths for generative models
N. Shaul, R. T. Chen, M. Nickel, M. Le, and Y. Lipman · 2023
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Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions
R. Singhal, M. Goldstein, and R. Ranganath · 2023
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Consistency models
Y. Song, P. Dhariwal, M. Chen, and I. Sutskever · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
A. Tong, N. Malkin, G. Huguet, Y. Zhang, J. Rector-Brooks, K. Fatras, G. Wolf, and Y. Bengio · 2023
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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
B. L. Trippe, J. Yim, D. Tischer, D. Baker, T. Broderick, R. Barzilay, and T. S. Jaakkola · 2023
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De novo design of protein structure and function with rfdiffusion
J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al · 2023
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One-step diffusion with distribution matching distillation
T. Yin, M. Gharbi, R. Zhang, E. Shechtman, F. Durand, W. T. Freeman, and T. Park · 2023
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Shiftddpms: exploring conditional diffusion models by shifting diffusion trajectories
Z. Zhang, Z. Zhao, J. Yu, and Q. Tian · 2023
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Fast sampling of diffusion models via operator learning
H. Zheng, W. Nie, A. Vahdat, K. Azizzadenesheli, and A. Anandkumar · 2023
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Improved techniques for maximum likelihood estimation for diffusion odes
K. Zheng, C. Lu, J. Chen, and J. Zhu · 2023
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Understanding diffusion objectives as the elbo with simple data augmentation
D. Kingma and R. Gao · 2024
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Generalized schrödinger bridge matching
G.-H. Liu, Y. Lipman, M. Nickel, B. Karrer, E. Theodorou, and R. T. Q. Chen · 2024
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Diffenc: Variational diffusion with a learned encoder
B. M. G. Nielsen, A. Christensen, A. Dittadi, and O. Winther · 2024
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Star-shaped denoising diffusion probabilistic models
A. Okhotin, D. Molchanov, A. Vladimir, G. Bartosh, V. Ohanesian, A. Alanov, and D. P. Vetrov · 2024
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Diffusion models with learned adaptive noise processes, 2024
S. S. Sahoo, A. Gokaslan, C. D. Sa, and V. Kuleshov · 2024
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Bespoke solvers for generative flow models
N. Shaul, J. Perez, R. T. Q. Chen, A. Thabet, A. Pumarola, and Y. Lipman · 2024
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Diffusion schrödinger bridge matching
Y. Shi, V. De Bortoli, A. Campbell, and A. Doucet · 2024
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Naturalspeech: End-to-end text-to-speech synthesis with human-level quality
X. Tan, J. Chen, H. Liu, J. Cong, C. Zhang, Y. Liu, X. Wang, Y. Leng, Y. Yi, L. He, et al · 2024
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Lossy image compression with conditional diffusion models
R. Yang and S. Mandt · 2024
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