CoChat
Sign Up
Cited by 1,704Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine LearningImage and Signal Denoising Methods

The DOI matches its Crossref record; 236 of the 236 references it cites were found in a registry or an index; none of the 245 sources its claims cite is checked for support yet.

Diffusion Models: A Comprehensive Survey of Methods and Applications

Open access
Yang, Ling, Zhang, Zhilong, Song, Yang, Hong, Shenda, Xu, Runsheng, Zhao, Yue, Zhang, Wentao, Cui, Bin, Yang, Ming-Hsuan·2024·ACM Computing Surveys
DOI 10.1145/3626235
Open this paper in CoChat — free accountOpen PDF

Abstract

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This survey organizes diffusion-model research into three core areas—efficient sampling, improved likelihood estimation, and handling data with special structures—and reviews connections to other generative models plus six application domains. It also introduces DDPMs, score-based generative models, and Score SDEs as the main formulations and outlines future research directions.

Key findings

  • The paper proposes a taxonomy of diffusion-model advances: efficient sampling, improved likelihood estimation, and methods for data with special structures such as discrete data, invariant structures, and manifold structures.
  • Efficient sampling methods are split into learning-free and learning-based approaches; customized ODE solvers such as DPM-solver and exponential integrators can produce high-quality samples in just 10 to 20 iterations, far fewer than the hundreds typically required without accelerated sampling.
  • Improved-likelihood methods include noise schedule optimization, reverse variance learning, and exact likelihood computation; Variational Diffusion Models show that noise schedules do not affect the VLB as long as they share the same values at \(R_{\min}\) and \(R_{\max}\), and only affect Monte Carlo estimator variance.
  • Applications are grouped into six categories: computer vision, natural language processing, temporal data modeling, multi-modal learning, robust learning, and interdisciplinary applications.

Methods

  • Design: a comprehensive survey/overview of diffusion models, not an empirical study with new experiments.
  • Data/sample: existing published research literature on diffusion models and related generative models, cited throughout the reference list.
  • Main analysis: conceptual and taxonomic synthesis, including a self-contained introduction to DDPMs, SGMs, and Score SDEs, followed by categorized review of methods, connections, applications, and future directions.

Limitations

  • Stated: the paper identifies open issues including revisiting assumptions such as complete information erasure in finite time, lack of theoretical understanding for why and when diffusion models work, weaker latent representations than VAEs/GANs, and pitfalls such as dataset bias, improper image generation, and privacy issues.
  • Stated: for scene-graph-to-image generation, high-quality paired SG-image datasets are scarce, and leveraging large-scale text-image datasets for augmentation or semantic diffusion priors remains an open problem.
  • Evident: the survey does not report a systematic search protocol, inclusion/exclusion criteria, or quantitative meta-analysis, so its coverage is broad but potentially selective.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3626235

Cites

236 of 236 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 245 cited sources checked · 0 supported · 0 flagged

Show all 100 claims

  • It may state two separate claims as one. 66
  • Probably not a substantial claim. 67
119 of 119

  • The paper relies on this without support.

    Over the past two years, the body of research on diffusion models has grown significantly, making it increasingly challenging for new researchers to stay abreast of recent developments.

    “Over the past two years, the body of research on diffusion models has grown significantly, making it increasingly challenging for new researchers to stay abreast of the recent developments in the field.”

  • The paper relies on this without support.

    The sheer volume of work on diffusion models can obscure major trends and hinder further research progress.

    “Additionally, the sheer volume of work can obscure major trends and hinder further research progress.”

  • The authors state this as an assumption.

    Langevin dynamics converges to its equilibrium at every noise level.

    “under the assumption that Langevin dynamics converges to its equilibrium at every noise level”

  • The paper relies on this without support.

    Text-to-image generation models struggle to faithfully reproduce complex sentences with many objects and relationships.

    “Despite text-to-image generation models have made significant progress, they struggle to faithfully reproduce complex sentences with many objects and relationships.”

  • The paper relies on this without support.

    High-quality paired scene-graph-image datasets are scarce.

    “However, highquality paired SG-image datasets are scarce.”

  • The paper relies on this without support.

    Diffusion models have emerged as a powerful framework, notably as the only one that can rival generative adversarial networks in most applications without resorting to adversarial training.

    “Diffusion models have emerged as a powerful framework, notably as the only one that can rival generative adversarial networks (GANs) in most applications without resorting to adversarial training.”

  • The paper relies on this without support.

    Unlike variational autoencoders or generative adversarial networks, diffusion models are less effective for providing good representations of data in their latent space.

    “Unlike variational autoencoders or generative adversarial networks, diffusion models are less effective for providing good representations of data in their latent space.”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Grad-TTS2 name disagrees with its cited reference

    Minor

    The text calls the model Grad-TTS2, but reference [118] is titled Guided-TTS 2.

  • Latent Diffusion Models is cited as DPM-solver

    Minor

    The citation attached to Latent Diffusion Models points to a bibliography entry for DPM-solver, not the paper identified elsewhere in the text as LDM [196].

  • GeoDiff citation year disagrees with the reference

    Minor

    The text dates reference [261] to 2022, while its bibliography entry gives 2021.

  • DDPM reverse SDE has the opposite score-term sign

    Minor

    Equation (18) subtracts the score term in a reverse-time SDE. Substituting the DDPM drift from Equation (16) therefore gives a negative score term, but Equation (21) gives it a positive sign.

  • Annealed Langevin sampling uses incompatible time-step ranges

    Minor

    The stated iteration runs from T down to 1, but the immediately following initialization rule applies from 0 up to T−1. As written, the rule does not initialize the first stated step, t = T, and the stated iteration does not perform the t = 0 step.

5 of 5

Citations

Cites

Flagged first, then by citations

Show all 199 references

Research works printed but not linked: 92

  1. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg. 2021. Structured denoising diffusion models in discrete state-spaces. In Advances in Neural Information Processing Systems.
  2. Hritik Bansal and Aditya Grover. 2023. Leaving reality to imagination: Robust classification via generated datasets. In International Conference on Learning Representations.
  3. Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang. 2021. Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models. In International Conference on Learning Representations.
  4. Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. 2021. Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations.
  5. Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton. 2018. Demystifying MMD GANs. In International Conference on Learning Representations.
  6. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems.
  7. Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. 2018. BRITS: Bidirectional recurrent imputation for time series. In Advances in Neural Information Processing Systems, Vol. 31.
  8. Tianrong Chen, Guan-Horng Liu, and Evangelos Theodorou. 2021. Likelihood training of Schrödinger bridge using forward-backward SDEs theory. In International Conference on Learning Representations.
  9. Rewon Child. 2020. Very deep VAEs generalize autoregressive models and can outperform them on images. In International Conference on Learning Representations.
  10. Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet. 2020. Relaxing bijectivity constraints with continuously indexed normalising flows. In International Conference on Machine Learning. 2133–2143.
  11. Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019. Plug and play language models: A simple approach to controlled text generation. In International Conference on Learning Representations.
  12. Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet. 2021. Diffusion Schrödinger bridge with applications to score-based generative modeling. In Advances in Neural Information Processing Systems, Vol. 34. 17695–17709.
  13. Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. In Advances in Neural Information Processing Systems, Vol. 34. 8780–8794.
  14. Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2017. Density estimation using Real NVP. In International Conference on Learning Representations.
  15. Tim Dockhorn, Arash Vahdat, and Karsten Kreis. 2021. Score-based generative modeling with critically-damped Langevin diffusion. In International Conference on Learning Representations.
  16. Vincent Fortuin, Dmitry Baranchuk, Gunnar Ratsch, and Stephan Mandt. 2020. GP-VAE: Deep probabilistic time series imputation. In International Conference on Artificial Intelligence and Statistics. 1651–1661.
  17. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning. 1263–1272.
  18. Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, and David Duvenaud. 2019. Scalable reversible generative models with free-form continuous dynamics. In International Conference on Learning Representations.
  19. Albert Gu, Karan Goel, and Christopher Re. 2021. Efficiently modeling long sequences with structured state spaces. In International Conference on Learning Representations.
  20. William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1025–1035.
  21. Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi. 2022. Is synthetic data from generative models ready for image recognition?. In International Conference on Learning Representations.
  22. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, Vol. 33. 6840–6851.
  23. Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans. 2021. Autoregressive diffusion models. In International Conference on Learning Representations.
  24. Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling. 2021. Argmax flows and multinomial diffusion: Learning categorical distributions. In Advances in Neural Information Processing Systems, Vol. 34. 12454–12465.
  25. Chin-Wei Huang, Jae Hyun Lim, and Aaron C. Courville. 2021. A variational perspective on diffusion-based generative models and score matching. In Advances in Neural Information Processing Systems, Vol. 34. 22863–22876.
  26. Long Jin, Justin Lazarow, and Zhuowen Tu. 2017. Introspective classification with convolutional nets. In Advances in Neural Information Processing Systems, Vol. 30. 823–833.
  27. Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. In International Conference on Machine Learning. 2323–2332.
  28. Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, and Koray Kavukcuoglu. 2018. Efficient neural audio synthesis. In International Conference on Machine Learning. 2410–2419.
  29. Boah Kim, Inhwa Han, and Jong Chul Ye. 2021. DiffuseMorph: Unsupervised deformable image registration along continuous trajectory using diffusion models. arXiv preprint arXiv:2112.05149 (2021). · arXiv
  30. Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. 2021. Variational diffusion models. In Advances in Neural Information Processing Systems, Vol. 34. 21696–21707.
  31. Hugo Larochelle and Iain Murray. 2011. The neural autoregressive distribution estimator. In International Conference on Artificial Intelligence and Statistics.
  32. Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning. PMLR, 12888–12900.
  33. Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In Advances in Neural Information Processing Systems, Vol. 32.
  34. Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. 2021. Pseudo numerical methods for diffusion models on manifolds. In International Conference on Learning Representations.
  35. Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. 2022. Maximum likelihood training for score-based diffusion ODEs by high order denoising score matching. In International Conference on Machine Learning. 14429–14460.
  36. Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang. 2021. Predicting molecular conformation via dynamic graph score matching. In Advances in Neural Information Processing Systems, Vol. 34. 19784–19795.
  37. Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, and Xiaojie Yuan. 2018. Multivariate time series imputation with generative adversarial networks. In Advances in Neural Information Processing Systems, Vol. 31.
  38. Zhaoyang Lyu, Zhifeng Kong, X. U. Xudong, Liang Pan, and Dahua Lin. 2021. A conditional point diffusion-refinement paradigm for 3D point cloud completion. In International Conference on Learning Representations.
  39. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations.
  40. Gábor Melis, Chris Dyer, and Phil Blunsom. 2018. On the state of the art of evaluation in neural language models. In International Conference on Learning Representations.
  41. Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2021. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations.
  42. Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, and Stefano Ermon. 2020. Improved autoregressive modeling with distribution smoothing. In International Conference on Learning Representations.
  43. Stephen Merity, Nitish Shirish Keskar, and Richard Socher. 2018. Regularizing and optimizing LSTM language models. In International Conference on Learning Representations.
  44. Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning. 8162–8171.
  45. Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning. 16784–16804.
  46. Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. 2020. Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics. 4474–4484.
  47. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations.
  48. Sung Woo Park, Kyungjae Lee, and Junseok Kwon. 2021. Neural Markov controlled SDE: Stochastic optimization for continuous-time data. In International Conference on Learning Representations.
  49. Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov. 2021. Grad-TTS: A diffusion probabilistic model for text-to-speech. In International Conference on Machine Learning. 8599–8608.
  50. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning. 8748–8763.
  51. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
  52. 10.5555/3455716.3455856 · DOI
  53. Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. 8821–8831.
  54. Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. 2021. Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting. In International Conference on Machine Learning. 8857–8868.
  55. Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M. Bergmann, and Roland Vollgraf. 2020. Multivariate probabilistic time series forecasting via conditioned normalizing flows. In International Conference on Learning Representations.
  56. Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In International Conference on Machine Learning. 1530–1538.
  57. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning. 1278–1286.
  58. Tim Salimans and Jonathan Ho. 2021. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations.
  59. Tim Salimans and Jonathan Ho. 2021. Should EBMs model the energy or the score?. In International Conference on Learning Representations.
  60. David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus. 2019. High-dimensional multivariate forecasting with low-rank Gaussian copula processes. In Advances in Neural Information Processing Systems, Vol. 32.
  61. Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, and Aaron van den Oord. 2021. Step-unrolled denoising autoencoders for text generation. In International Conference on Learning Representations.
  62. Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal. 2021. Robust learning meets generative models: Can proxy distributions improve adversarial robustness?. In International Conference on Learning Representations.
  63. Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang. 2021. Learning gradient fields for molecular conformation generation. In International Conference on Machine Learning. 9558–9568.
  64. Ikaro Silva, George Moody, Daniel J. Scott, Leo A. Celi, and Roger G. Mark. 2012. Predicting in-hospital mortality of ICU patients: The physionet/computing in cardiology challenge 2012. In 2012 Computing in Cardiology. 245–248.
  65. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, and Oran Gafni. 2022. Make-a-Video: Text-to-video generation without text-video data. In International Conference on Learning Representations.
  66. Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning. 2256–2265.
  67. Xizewen Han Huangjie Zheng and Mingyuan Zhou. 2022. Card: Classification and regression diffusion models. Advances in Neural Information Processing Systems 35 (2022) 18100–18115.
  68. Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. In International Conference on Learning Representations.
  69. Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon. 2021. Maximum likelihood training of score-based diffusion models. In Advances in Neural Information Processing Systems, Vol. 34. 1415–1428.
  70. Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, Vol. 32.
  71. Yang Song and Stefano Ermon. 2020. Improved techniques for training score-based generative models. In Advances in Neural Information Processing Systems, Vol. 33. 12438–12448.
  72. Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon. 2019. Sliced score matching: A scalable approach to density and score estimation. In The Conference on Uncertainty in Artificial Intelligence. 204.
  73. Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. 2021. Solving inverse problems in medical imaging with score-based generative models. In International Conference on Learning Representations.
  74. Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2020. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations.
  75. Xuan Su, Jiaming Song, Chenlin Meng, and Stefano Ermon. 2022. Dual diffusion implicit bridges for image-to-image translation. In International Conference on Learning Representations.
  76. Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. 2021. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. In Advances in Neural Information Processing Systems, Vol. 34. 24804–24816.
  77. Brandon Trabucco, Kyle Doherty, Max Gurinas, and Ruslan Salakhutdinov. 2023. Effective data augmentation with diffusion models. In International Conference on Learning Representations.
  78. Arash Vahdat, Karsten Kreis, and Jan Kautz. 2021. Score-based generative modeling in latent space. In Advances in Neural Information Processing Systems, Vol. 34. 11287–11302.
  79. Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. In International Conference on Machine Learning. 1747–1756.
  80. Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi. 2021. Learning fast samplers for diffusion models by differentiating through sample quality. In International Conference on Learning Representations.
  81. Hao Wu, Jonas Köhler, and Frank Noe. 2020. Stochastic normalizing flows. In Advances in Neural Information Processing Systems, Vol. 33. 5933–5944.
  82. Pan Xie, Qipeng Zhang, Zexian Li, Hao Tang, Yao Du, and Xiaohui Hu. 2022. Vector quantized diffusion model with CodeUnet for text-to-sign pose sequences generation. arXiv preprint arXiv:2208.09141 (2022). · arXiv
  83. Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S. Jaakkola. 2021. Crystal diffusion variational autoencoder for periodic material generation. In International Conference on Learning Representations.
  84. Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang. 2021. Self-supervised graph-level representation learning with local and global structure. In International Conference on Machine Learning. 11548–11558.
  85. Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang. 2021. GeoDiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations.
  86. Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In International Conference on Machine Learning. 25038–25054.
  87. Jongmin Yoon, Sung Ju Hwang, and Juho Lee. 2021. Adversarial purification with score-based generative models. In International Conference on Machine Learning. 12062–12072.
  88. Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. In Advances in Neural Information Processing Systems, Vol. 32.
  89. Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, and Ying Nian Wu. 2022. Latent diffusion energy-based model for interpretable text modelling. In International Conference on Machine Learning. 25702–25720.
  90. Qinsheng Zhang and Yongxin Chen. 2021. Diffusion normalizing flow. In Advances in Neural Information Processing Systems, Vol. 34. 16280–16291.
  91. Wenrui Zhang, Ling Yang, Shijia Geng, and Shenda Hong. 2022. Cross reconstruction transformer for self-supervised time series representation learning. arXiv preprint arXiv:2205.09928 (2022). · arXiv
  92. Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022. Truncated diffusion probabilistic models. arXiv preprint arXiv:2202.09671 (2022). · arXiv

Cited by

Sorted by citations

Items 201–300 of 1,004

Related

See all

Metadata

Authors
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H.
Affiliations
  • Peking University (CN)
  • OpenAI (United States) (US)
  • University of California, Los Angeles (US)
  • University of Southern California (US)
  • HEC Montréal (CA)
  • University of California, Merced (US)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2023-11-09
Volume
56
Issue
4
Pages
1-39
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3626235
OpenAlex
W4387195417
Cited by
1,704
References
199
Citations by year
2026: 392 · 2025: 683 · 2024: 483 · 2023: 142
FWCI
154.02
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords
diffusion models, deep generative models, efficient sampling, likelihood estimation, structured data, hybrid generative models, image generation, video generation, temporal data modeling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review