MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse. To address this, we introduce MIC, a framework that optimizes the geometric landscape of multi-granular embeddings through isotropic subspace alignment. MIC employs Soft Collapse Regularization (SCR) to mitigate redundancy between prefix and residual subspaces via cross-correlation penalties, alongside Spectral Isotropy Regularization (SIR) to ensure hyper-spherical uniformity in low-dimensional prefixes. By unifying these strategies through a self-distillation objective, MIC generates semantically dense representations that m...
| Authors | Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham |
|---|---|
| Primary category | cs.LG |
| Published | 28 May 2026 |
| Last updated at source | 31 May 2026 |
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