MaDeL: Manifold-Decomposed Feature Losses for Generative Modeling
Generative models are often trained with isotropic objectives such as mean-squared error. For data concentrated near a low-dimensional manifold, however, such losses conflate displacement along the manifold, which may represent valid variation, with displacement away from it, which produces invalid samples. This mismatch is especially problematic in sparse, highly constrained domains, where ambient-space regression can encourage off-manifold interpolation. We ask whether a generative objective can distinguish manifold-parallel variation from manifold-orthogonal deviation directly from data, without explicitly estimating the manifold. We introduce a manifold-decomposed feature loss (MaDeL)...
| Authors | Beomsu Kim, Jong Chul Ye, Kwanyoung Kim |
|---|---|
| Primary category | cs.LG |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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