On the Feature Learning in Diffusion Models

2024-12-02

arXiv.org

AI Summary

This paper investigates the fundamental differences in representation learning mechanisms between diffusion models and classification models, specifically addressing whether diffusion models learn more balanced and comprehensive data representations. Method: We establish the first theoretical framework to comparatively analyze their feature learning dynamics, proving that the denoising objective implicitly optimizes feature balance and completeness. Combining theoretical analysis with experiments on both synthetic and real-world datasets—under standard U-Net architectures—we validate the mechanism via feature visualization and interpretability evaluation. Results: Diffusion models exhibit significantly enhanced inter-class separability and intra-class consistency in intermediate-layer features, outperforming structurally identical classification models. Our core contribution is uncovering the intrinsic regularizing effect of the denoising objective on representation quality, offering a novel theoretical perspective and empirical evidence for understanding the superior representational capacity of diffusion models.

Technology Category

Computer Vision: Diffusion Models for VisionMachine Learning: Representation LearningSearch and Optimization: Learning to Search

Application Category

Web Mining and Content Analysis: Large pretrained models with web dataGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Analyze feature learning dynamics in diffusion models.
• Compare diffusion models with classification models.
• Demonstrate balanced data representation in diffusion models.

Innovation

• Feature learning framework for diffusion models
• Denoising objective promotes balanced data representations
• Empirical validation on synthetic and real datasets