Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
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 that this condition yields identifiability of general nonlinear generative models, without requiring statistical independence or causal assumptions, provided the latent domain admits all combinations of factor values. Experiments with orthogonality-regularized normalizing flows empirically confirm the theory, demonstrate reliable recovery of ground-truth factors, and shed light on the success of...
| Authors | Mathieu Cyrille Simon, Pascal Frossard, Christophe De Vleeschouwer |
|---|---|
| Primary category | cs.LG |
| Published | 19 June 2026 |
| Last updated at source | 8 August 2026 |
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