Latent Diffusion for Missing Data
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-...
| Authors | Alberte Heering Estad, Ignacio Peis, Jes Frellsen |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 19 June 2026 |
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