提出新方法提升扩散模型在低噪声下的训练稳定性与生成质量。
Latent Target Score Matching, with an application to Simulation-Based Inference
- 用联合信号间接监督边缘得分，解决清洁数据得分不可得问题。
- 在模拟推断任务中显著降低方差，提升得分精度和样本质量。
- 适合需要高精度生成或含潜变量建模的研究者使用。
扩散模型的去噪得分匹配（DSM）在低噪声水平下常因方差过高而表现不佳。目标得分匹配（TSM）通过利用干净数据的得分信息可实现低方差训练，但其前提是在许多应用中因存在潜变量导致清洁得分不可获取，仅能观测联合信号。本文提出潜在目标得分匹配（LTSM），将TSM扩展至利用联合得分对边缘得分进行低方差监督。尽管LTSM在低噪声下表现优异，但与DSM混合使用可确保在全噪声尺度上的鲁棒性。在多个基于模拟的推断任务中，LTSM持续改善了方差、得分准确性和样本质量。
原文摘要 · Abstract (English)
Denoising score matching (DSM) for training diffusion models may suffer from high variance at low noise levels. Target Score Matching (TSM) mitigates this when clean data scores are available, providing a low-variance objective. In many applications clean scores are inaccessible due to the presence of latent variables, leaving only joint signals exposed. We propose Latent Target Score Matching (LTSM), an extension of TSM to leverage joint scores for low-variance supervision of the marginal score. While LTSM is effective at low noise levels, a mixture with DSM ensures robustness across noise scales. Across simulation-based inference tasks, LTSM consistently improves variance, score accuracy, and sample quality.
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