Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.
High-performing neural network models of visual cortex benefit from high latent dimensionality
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Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.