mlsanityData to 7 Oct 2026

Generative Modeling with Diffusion

arXiv 2412.10948 ↗First posted 14 Dec 2024stat.ML cs.LG math.PRcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We provide an overview of the diffusion model as a method to generate new samples. Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT). Diffusion models in particular apply noise to sample data and then "reverse" this noising process to generate new samples. We will formally define these noising and denoising processes, then present algorithms to train and generate with a diffusion model. Afterward, we will explore a potential application of diffusion models in improving classifier performance on imbalanced data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 10 of its 19 references that are in the map. First posted on arXiv 14 Dec 2024.

Builds on

10 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  7. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  8. 2021TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Upcited by 162
  9. 2023Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyondcited by 139

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  1. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
  2. 2024Theoretical research on generative diffusion models: an overview
  3. 2024GUD: Generation with Unified Diffusion
  4. 2024Generative Diffusion Modeling: A Practical Handbook
  5. 2024An overview of diffusion models for generative artificial intelligence
  6. 2025Diffusion Models: A Mathematical Introduction
  7. 2025Understanding Diffusion Models via Code Execution
  8. 2023Diffusion Models in NLP: A Survey