Spherical VAE with Cluster-Aware Feasible Regions: Guaranteed Prevention of Posterior Collapse
Variational autoencoders (VAEs) frequently suffer from posterior collapse, where the latent variables become uninformative as the approximate posterior degenerates to the prior. While recent work has characterized collapse as a phase transition determined by data covariance properties, existing approaches primarily aim to avoid rather than eliminate collapse. We introduce a novel framework that theoretically guarantees non-collapsed solutions by leveraging spherical shell geometry and cluster-aware constraints. Our method transforms data to a spherical shell, computes optimal cluster assignments via K-means, and defines a feasible region between the within-cluster variance $W$ and collaps...
| Authors | Zegu Zhang, Jian Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 18 May 2026 |
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