CPF-GCD enforces low-rank compositional structure on vision backbone features via spatial primitive fields so that novel categories emerge as new activation patterns over a shared vocabulary of reusable visual primitives.
arXiv preprint arXiv:2406.09366 , year=
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MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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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Identifying Latent Concepts and Structures for Generalized Category Discovery
CPF-GCD enforces low-rank compositional structure on vision backbone features via spatial primitive fields so that novel categories emerge as new activation patterns over a shared vocabulary of reusable visual primitives.
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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.