Representation Alignment from a Few Paired Data via Distilled Optimal Transport
arXiv cs.AIen
arXiv cs.AI
AI Global WirearXiv:2610.12781v1 Announce Type: new Abstract: Aligning two independently pretrained unimodal encoders typically demands millions of paired examples. In this paper, we study the regime where only $N \sim 100$--$500$ pairs are available, while unpaired data on either side is abundant. Our method expresses every representation by its similarities to the $N$ paired anchors, which places both modalities in a common coordinate system; matches the two point clouds with a single entropic optimal transport problem solved over the entire pool, whose marginal constraints counteract the hubness that defeats nearest-neighbor matching; and distills the resulting plan into two lightweight encoders, so th
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
Related AI news
- EvoBelief: Falsifiable World Models for Self-Improving LLM AgentsarXiv cs.AI · October 12, 2026
- Neural networks are more modular than single neurons suggestarXiv cs.AI · October 12, 2026
- Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial EditingarXiv cs.AI · October 12, 2026
- OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action ModelsarXiv cs.AI · October 12, 2026
- CLEAT: Learning Value-Guided Clarification for Interactive AgentsarXiv cs.AI · October 12, 2026
- DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial NetworksarXiv cs.AI · October 12, 2026