将扩散模型引入潜在空间，提升数据缺失时的生成稳定性。
Latent Diffusion for Missing Data
- 先用鲁棒VAE提取不完整数据的语义特征，再在潜在空间训练扩散模型。
- 在50%缺失率下仍保持高质量样本，像素空间模型性能随缺失率下降。
- 适合处理高缺失率数据，尤其适用于医学图像等敏感场景。
扩散模型已成为强大的缺失数据填补方法，但现有方法多直接在数据空间操作，在训练数据严重缺失时表现下降。本文探究将扩散模型转移到学习到的潜在表示是否能增强在随机完全缺失（MCAR）情况下的鲁棒性。为此，提出两阶段框架：首先通过鲁棒的基于VAE的填补器从不完整观测中学习紧凑的语义特征，然后在所得潜在空间训练扩散模型。在不同缺失率下，与相同不完整数据设置下的像素空间扩散模型进行受控对比。潜空间扩散模型在高达50%缺失度时仍保持高样本质量且稳定，而像素空间扩散模型随缺失率增加性能持续下降。下游填补任务中，潜空间扩散模型也始终优于像素空间扩散模型。结果表明，潜空间建模可缓解零值填充输入带来的伪影放大问题，并为不完整数据学习提供更鲁棒的生成先验。总体而言，研究支持潜空间扩散作为缺失数据问题中像素空间扩散的有力实用替代方案。
原文摘要 · Abstract (English)
Diffusion models have emerged as powerful generative approaches for missing-data imputation, yet most existing methods operate directly in data space and degrade when training data are heavily incomplete. We investigate whether shifting diffusion to a learned latent representation improves robustness under missing-completely-at-random (MCAR) corruption. To this end, we propose a two-stage framework: a robust VAE-based imputer first learns compact semantic features from incomplete observations, and a diffusion model is then trained in the resulting latent space. Across training missing rates, we perform a controlled comparison against pixel-space diffusion models under the same incomplete-data setting. The latent diffusion model maintains high sample quality and remains stable up to 50% missingness, while pixel-space diffusion degrades progressively as missingness increases. For downstream imputation, latent diffusion also achieves consistently better performance than pixel-space diffusion. These findings indicate that latent-space modeling mitigates artifact amplification from zero-imputed inputs and provides a more robust generative prior for incomplete-data learning. Overall, our results support latent diffusion as a strong and practically useful alternative to pixel-space diffusion for missing-data problems.
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