MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.
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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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On the Capacity of Distinguishable Synthetic Identity Generation under Face Verification
The capacity of distinguishable synthetic identity generation under face verification is characterized by spherical-code problems on the unit hypersphere, with lower bounds derived for both deterministic and stochastic generation models.