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AI Research
arXiv CS.AI·Jun 10·5 min read
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
TL;DR
Pre-trained diffusion models often fail to generate outputs that match user-specific target data, especially when retraining is not feasible. MMD Guidance is a training-free method that enhances the reverse diffusion process using Maximum Mean Discrepancy (MMD) to align generated samples with a reference dataset.
✦ Why It Matters
Engineers can use MMD Guidance to improve the adaptability of generative models without the need for retraining.
Key Takeaways
- Use MMD Guidance for training-free adaptation of diffusion models.
- Leverage MMD for reliable distribution estimates from limited data.
- Apply guidance in latent space for computational efficiency.
- Achieve high sample fidelity while aligning with target distributions.
How It Works
MMD Guidance integrates the Maximum Mean Discrepancy metric into the reverse diffusion process, allowing the model to adjust its sampling trajectory based on the distribution of a reference dataset. By calculating gradients from MMD, the model can effectively steer its outputs to better match the desired characteristics of the target data, all without requiring additional training.