On the feature learning in diffusion models
Keywords: Diffusion model, Feature learning
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, under identical settings, neural networks trained for classification tend to prioritize learning specific patterns in the data, often focusing on easy-to-learn features. In contrast, diffusion models, due to the denoising objective, are encouraged to learn more balanced and comprehensive representations of the data. To support these theoretical insights, we conduct several experiments on both synthetic and real-world datasets, which empirically validate our findings and underscore the distinct feature learning dynamics in diffusion models compared to classification.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 4393
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