Geometry-Aware Hyperbolic Residual-Quantized Variational Autoencoders
Residual Vector Quantization turns continuous representations into discrete, multi-level token sequences. Yet most methods operate in Euclidean space, despite the coarse-to-fine structure of the resulting codes and the latent hierarchies present in many data domains. Hyperbolic geometry offers a natural alternative for hierarchical representations, but naive hyperbolic extensions introduce geometric inconsistencies: non-associative hyperbolic addition prevents consistent residual aggregation, while standard straight-through gradient estimation ignores the geometry of the latent space. We propose a geometry-aware hyperbolic residual quantization that addresses these issues in both the forw...
| Authors | Alessio Colombo, Melika Ayoughi |
|---|---|
| Primary category | cs.LG |
| Published | 22 September 2026 |
| Last updated at source | 25 September 2026 |
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