mlsanityData to 7 Oct 2026

Theoretical research on generative diffusion models: an overview

arXiv 2404.09016 ↗First posted 13 Apr 2024cs.LG cs.AI cs.CVcited by 2

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on the specific application areas without concentrating on the research about the algorithm. Unlike them we investigated the theoretical developments of the generative diffusion models. These approaches mainly divide into two: training-based and sampling-based. Awakening to this allowed us a clear and understandable categorization for the researchers who will make new developments in the future.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 33 of its 46 references that are in the map. First posted on arXiv 13 Apr 2024.

Builds on

33 of 46 references.

Closest ancestors · 17

Same direction, newest first

  1. 2022Fast Sampling of Diffusion Models via Operator Learning24 Nov
  2. 2022Accelerating Diffusion Sampling with Classifier-based Feature Distillation22 Nov
  3. 2022Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching16 Jun
  4. 2022Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders19 Feb
  5. 2022Understanding DDPM Latent Codes Through Optimal Transport14 Feb
  6. 2021Diffusion Normalizing Flow14 Oct
  7. 2021Structured Denoising Diffusion Models in Discrete State-Spaces7 Jul
  8. 2021Variational Diffusion Models1 Jul
  9. 2021Score-based Generative Modeling in Latent Space10 Jun
  10. 2021Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation10 Jun
  11. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matching5 Jun
  12. 2021Cascaded Diffusion Models for High Fidelity Image Generation30 May
5 more
  1. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  2. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  3. 2021Maximum Likelihood Training of Score-Based Diffusion Models22 Jan
  4. 2020Denoising Diffusion Probabilistic Models19 Jun
  5. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 16

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2017Neural Discrete Representation Learningcited by 4,650
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  5. 2016Improved Techniques for Training GANscited by 2,938
  6. 2020Knowledge Distillation: A Surveycited by 1,578
  7. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  8. 2016Pixel Recurrent Neural Networkscited by 814
  9. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 566
  10. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 381
  11. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 263
  12. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
4 more
  1. 2022ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speechcited by 147
  2. 2022Poisson Flow Generative Modelscited by 95
  3. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  4. 2022Semi-Parametric Neural Image Synthesiscited by 36

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equationscited by 3
  2. 2026Denoise Stepwise Signals by Diffusion Model Based Approachcited by 0

Similar, not cited

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  3. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
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  5. 2023On the Generalization of Diffusion Model
  6. 2022Let us Build Bridges: Understanding and Extending Diffusion Generative Models
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