HyDeS introduces hyperspherical density shaping with a von Mises-Fisher estimator to create theoretically grounded self-supervised representations that focus on foreground features.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Anisotropic self-supervised vision representations degrade approximate nearest-neighbor retrieval performance while more isotropic ones with local purity improve it.
citing papers explorer
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Self-Supervised Representation Learning via Hyperspherical Density Shaping
HyDeS introduces hyperspherical density shaping with a von Mises-Fisher estimator to create theoretically grounded self-supervised representations that focus on foreground features.
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Geometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval
Anisotropic self-supervised vision representations degrade approximate nearest-neighbor retrieval performance while more isotropic ones with local purity improve it.