A geometry-aware dataset condensation technique reformulates subset selection as one-sided partial optimal transport alignment plus regularization to improve diffusion model training fidelity.
arXiv preprint arXiv:2501.18901 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
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RADAR is a geometrically grounded metric that predicts cross-domain transferability by comparing layer-wise representation trajectory distributions in foundation models.
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Geometry-Aware Dataset Condensation for Diffusion Model Training
A geometry-aware dataset condensation technique reformulates subset selection as one-sided partial optimal transport alignment plus regularization to improve diffusion model training fidelity.
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RADAR: Relative Angular Divergence Across Representations
RADAR is a geometrically grounded metric that predicts cross-domain transferability by comparing layer-wise representation trajectory distributions in foundation models.