2026
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
ICML 2026poster
This paper explores unsupervised disentangled representation learning from a functional perspective. We define latent concepts as factors that influence observations through locally orthogonal directions, formalized as an orthogonality constraint on the Jacobian of the generative mapping. We prove t…