Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds
We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds. While diffusion models (DMs) have achieved state-of-the-art results in high-dimensional data synthesis, they rely on large training datasets and ignore intrinsic geometric structure. Latent diffusion models (LDMs) address the high dimensionality by learning a latent space, but they typically impose a Euclidean structure, failing to capture the underlying manifold geometry, especially problematic in data-sparse regimes. ILDM addresses these limitations by interpreting the latent space as a chart of...
| Authors | Yizhu Wang, Mu Niu, Xiaochen Yang |
|---|---|
| Primary category | stat.ML |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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