Universal Latent Homeomorphic Manifolds: A Framework for Cross-Domain Representation Unification

TL;DR

This paper introduces the Universal Latent Homeomorphic Manifold framework that unifies semantic and observation-driven representations into a single structure, enabling robust cross-domain transfer, zero-shot learning, and theoretical guarantees.

Contribution

It establishes homeomorphism as a criterion for unifying diverse latent manifolds and develops practical algorithms for verifying this structure from finite samples.

Findings

Achieved sparse image recovery with only 5% pixels in CelebA and MNIST.

Enabled cross-domain classifier transfer with 86.73% accuracy from MNIST to Fashion-MNIST.

Performed zero-shot classification on CIFAR-10 with 78.76% accuracy.

Abstract

We present the Universal Latent Homeomorphic Manifold (ULHM), a framework that unifies semantic representations (e.g., human descriptions, diagnostic labels) and observation-driven machine representations (e.g., pixel intensities, sensor readings) into a single latent structure. Despite originating from fundamentally different pathways, both modalities capture the same underlying reality. We establish \emph{homeomorphism}, a continuous bijection preserving topological structure, as the mathematical criterion for determining when latent manifolds induced by different semantic-observation pairs can be rigorously unified. This criterion provides theoretical guarantees for three critical applications: (1) semantic-guided sparse recovery from incomplete observations, (2) cross-domain transfer learning with verified structural compatibility, and (3) zero-shot compositional learning via valid transfer from semantic to observation space. Our framework learns continuous manifold-to-manifold transformations through conditional variational inference, avoiding brittle point-to-point mappings. We develop practical verification algorithms, including trust, continuity, and Wasserstein distance metrics, that empirically validate homeomorphic structure from finite samples. Experiments demonstrate: (1) sparse image recovery from 5% of CelebA pixels and MNIST digit reconstruction at multiple sparsity levels, (2) cross-domain classifier transfer achieving 86.73% accuracy from MNIST to Fashion-MNIST without retraining, and (3) zero-shot classification on unseen classes achieving 78.76% on CIFAR-10. Critically, the homeomorphism criterion determines when different semantic-observation pairs share compatible latent structure, enabling principled unification into universal representations and providing a mathematical foundation for decomposing general foundation models into domain-specific components.