用混合模型提升扩散模型在逆问题中的推理精度。
A Mixture-Based Framework for Guiding Diffusion Models
- 提出基于混合分布的后验近似方法，改进扩散模型在逆问题中的采样。
- 在图像修复、超分辨率及音频分离任务中均实现性能提升。
- 适用于无需重训练的高效推理，适合研究者快速部署于新任务。
去噪扩散模型在贝叶斯逆问题领域取得了显著进展。近期方法利用预训练扩散模型作为先验，仅依赖推理时计算，无需在相同数据集上重新训练特定任务模型。为逼近贝叶斯逆问题的后验分布，扩散模型从一系列中间后验分布中采样，而这些分布的似然函数不可解析。本文提出一种新的中间分布混合近似方法。由于混合分布的梯度采样因不可解析项而无法直接进行，我们设计了一种基于吉布斯采样的实用方案。通过大量实验验证了该方法的有效性，涵盖像素空间与潜在空间扩散先验的图像逆问题，以及使用音频扩散模型的源分离任务。代码已开源。
原文摘要 · Abstract (English)
Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide range of such problems, only leveraging inference-time compute and thereby eliminating the need to retrain task-specific models on the same dataset. To approximate the posterior of a Bayesian inverse problem, a diffusion model samples from a sequence of intermediate posterior distributions, each with an intractable likelihood function. This work proposes a novel mixture approximation of these intermediate distributions. Since direct gradient-based sampling of these mixtures is infeasible due to intractable terms, we propose a practical method based on Gibbs sampling. We validate our approach through extensive experiments on image inverse problems, utilizing both pixel- and latent-space diffusion priors, as well as on source separation with an audio diffusion model. The code is available at https://www.github.com/badr-moufad/mgdm
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