mlsanityData to 7 Oct 2026

On the Feature Learning in Diffusion Models

arXiv 2412.01021 ↗First posted 2 Dec 2024stat.ML cs.LGcited by 19

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The predominant success of diffusion models in generative modeling has spurred significant interest in understanding their theoretical foundations. In this work, we propose a feature learning framework aimed at analyzing and comparing the training dynamics of diffusion models with those of traditional classification models. Our theoretical analysis demonstrates that diffusion models, due to the denoising objective, are encouraged to learn more balanced and comprehensive representations of the data. In contrast, neural networks with a similar architecture trained for classification tend to prioritize learning specific patterns in the data, often focusing on easy-to-learn components. To support these theoretical insights, we conduct several experiments on both synthetic and real-world datasets, which empirically validate our findings and highlight the distinct feature learning dynamics in diffusion models compared to classification.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 18 of its 69 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

18 of 69 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024Diffusion Models and Representation Learning: A Survey30 Jun
  2. 2024Evaluating the design space of diffusion-based generative models18 Jun
  3. 2024Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel3 Jun
  4. 2024Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference26 May
  5. 2024Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity24 May
  6. 2024Learning general Gaussian mixtures with efficient score matching29 Apr
  7. 2024Learning Mixtures of Gaussians Using Diffusion Models29 Apr
  8. 2024A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data26 Feb
  9. 2024Towards a mathematical theory for consistency training in diffusion models12 Feb
  10. 2023Diffusion Models Beat GANs on Image Classification17 Jul
  11. 2023Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models15 Jun

Other references · 7

  1. 2024Your Diffusion Model is Secretly a Certifiably Robust Classifiercited by 24
  2. 2024Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalizationcited by 17
  3. 2021Analysis of feature learning in weight-tied autoencoders via the mean field lenscited by 13
  4. 2023Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptronscited by 12
  5. 2024On the Comparison between Multi-modal and Single-modal Contrastive Learningcited by 10
  6. 2023Per-Example Gradient Regularization Improves Learning Signals from Noisy Datacited by 7
  7. 2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depthcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 13; 2026: 6.

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  1. 2025FantasyWorld: Geometry-Consistent World Modeling via Unified Video and 3D Predictioncited by 33
  2. 2025Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modelingcited by 18
  3. 2025Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?cited by 15
  4. 2025Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generationcited by 11
  5. 2025Generalization of Diffusion Models Arises with a Balanced Representation Spacecited by 10
  6. 2025Generalization Dynamics of Linear Diffusion Modelscited by 9
  7. 2025On the Role of Label Noise in the Feature Learning Processcited by 4
  8. 2025How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?cited by 4
  9. 2025Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Modelscited by 2
  10. 2025Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noisecited by 2
  11. 2025Understanding Private Learning From Feature Perspectivecited by 2
  12. 2025On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Modelscited by 1
7 more
  1. 2026Diffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Studycited by 1
  2. 2026Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspectivecited by 1
  3. 2025Personalized Federated Training of Diffusion Models with Privacy Guaranteescited by 0
  4. 2026Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMscited by 0
  5. 2026On the Memorization of Consistency Distillation for Diffusion Modelscited by 0
  6. 2026Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refinecited by 0
  7. 2026DiffReID: Discriminative Diffusion Model for Object Re-Identificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Feature Information Dynamics in Diffusion
  2. 2025Diffusion Models: A Mathematical Introduction
  3. 2026Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
  4. 2026Understanding diffusion models requires rethinking (again) generalization
  5. 2024Generative Diffusion Modeling: A Practical Handbook
  6. 2024Theoretical research on generative diffusion models: an overview
  7. 2023Diffusion Models for Time Series Applications: A Survey
  8. 2022Let us Build Bridges: Understanding and Extending Diffusion Generative Models