SOURCE-LINKED INTELLIGENCE
Beyond Uniform Local Isometry and Topology: FactoMap for Disentangled Representations
Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collapse, or have position-dependent geometry. We introduce factor-space structure, combining factor domains, generator-induced identifications, and position-dependent scales to distinguish topologically equivalent spaces with different factor geometries. We show that statistically independent factors need not be geometrically separable: hue and scale produce effects that grow at different rates, yielding anisotropy that no fixed rescaling removes. We p
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- arXiv · AI, language, vision and robotics · 2026-08-25T15:59:59.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.