# On the Feature Learning in Diffusion Models

arXiv: https://arxiv.org/abs/2412.01021 · HTML: https://mlsanity.com/paper/2412.01021/

## Key facts

- Cited by 20 papers in the map (data through 14 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.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 18 of 18)

- [A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data](https://mlsanity.com/paper/2402.16991/) (2024)
- [Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models](https://mlsanity.com/paper/2306.09251/) (2023)
- [Diffusion Models Beat GANs on Image Classification](https://mlsanity.com/paper/2307.08702/) (2023)
- [Diffusion Models and Representation Learning: A Survey](https://mlsanity.com/paper/2407.00783/) (2024)
- [Learning general Gaussian mixtures with efficient score matching](https://mlsanity.com/paper/2404.18893/) (2024)
- [Learning Mixtures of Gaussians Using Diffusion Models](https://mlsanity.com/paper/2404.18869/) (2024)
- [Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity](https://mlsanity.com/paper/2405.15986/) (2024)
- [Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel](https://mlsanity.com/paper/2406.00924/) (2024)
- [Towards a mathematical theory for consistency training in diffusion models](https://mlsanity.com/paper/2402.07802/) (2024)
- [Evaluating the design space of diffusion-based generative models](https://mlsanity.com/paper/2406.12839/) (2024)
- [Your Diffusion Model is Secretly a Certifiably Robust Classifier](https://mlsanity.com/paper/2402.02316/) (2024)
- [Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference](https://mlsanity.com/paper/2405.16387/) (2024)
- [Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization](https://mlsanity.com/paper/2409.19345/) (2024)
- [Analysis of feature learning in weight-tied autoencoders via the mean field lens](https://mlsanity.com/paper/2102.08373/) (2021)
- [Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons](https://mlsanity.com/paper/2306.13926/) (2023)
- [On the Comparison between Multi-modal and Single-modal Contrastive Learning](https://mlsanity.com/paper/2411.02837/) (2024)
- [Per-Example Gradient Regularization Improves Learning Signals from Noisy Data](https://mlsanity.com/paper/2303.17940/) (2023)
- [Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth](https://mlsanity.com/paper/2402.05013/) (2024)

## Built on by (most cited first, 20 of 20)

- [FantasyWorld: Geometry-Consistent World Modeling via Unified Video and 3D Prediction](https://mlsanity.com/paper/2509.21657/) (2025)
- [Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling](https://mlsanity.com/paper/2502.05743/) (2025)
- [Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?](https://mlsanity.com/paper/2502.04725/) (2025)
- [Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation](https://mlsanity.com/paper/2505.22391/) (2025)
- [Generalization of Diffusion Models Arises with a Balanced Representation Space](https://mlsanity.com/paper/2512.20963/) (2025)
- [Generalization Dynamics of Linear Diffusion Models](https://mlsanity.com/paper/2505.24769/) (2025)
- [On the Role of Label Noise in the Feature Learning Process](https://mlsanity.com/paper/2505.18909/) (2025)
- [How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?](https://mlsanity.com/paper/2510.17526/) (2025)
- [Understanding Private Learning From Feature Perspective](https://mlsanity.com/paper/2511.18006/) (2025)
- [Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models](https://mlsanity.com/paper/2505.02824/) (2025)
- [Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise](https://mlsanity.com/paper/2509.18001/) (2025)
- [On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models](https://mlsanity.com/paper/2508.16154/) (2025)
- [Diffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Study](https://mlsanity.com/paper/2601.13416/) (2026)
- [Provable Effects of Data Replay in Continual Learning: A Feature Learning Perspective](https://mlsanity.com/paper/2602.02767/) (2026)
- [Benign Overfitting in Adversarial Training for Vision Transformers](https://mlsanity.com/paper/2604.19724/) (2026)
- [Personalized Federated Training of Diffusion Models with Privacy Guarantees](https://mlsanity.com/paper/2504.00952/) (2025)
- [Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs](https://mlsanity.com/paper/2604.10074/) (2026)
- [On the Memorization of Consistency Distillation for Diffusion Models](https://mlsanity.com/paper/2604.23552/) (2026)
- [Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine](https://mlsanity.com/paper/2605.20235/) (2026)
- [DiffReID: Discriminative Diffusion Model for Object Re-Identification](https://mlsanity.com/paper/2609.36894/) (2026)

## 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.