Preprint Open access
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 mismat …