Spectral Isotropy Regularization
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MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
arXiv:2605.29987v2 Announce Type: replace Abstract: 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.