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papersTODAY 04:00 UTC

PACE Introduces Progressive Angular-to-Norm Contrastive Embedding for Multimodal Models

Researchers propose PACE, a training method for multimodal embedding models that shifts the contrastive objective from an angular (cosine-based) formulation toward a norm-based one over the course of training. The approach is presented as an alternative to standard cosine contrastive objectives, which the authors say offer stable but potentially limited training dynamics. The work is posted as an arXiv preprint in the cs.AI and cs.LG categories.

arXivPACEcontrastive learningcosine similaritymultimodal embedding models

COVERAGE · 3 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.CLPACE: Progressive Angular-to-Norm Contrastive Embedding ↗TODAY 04:00 UTC
arXiv cs.AIPACE: Progressive Angular-to-Norm Contrastive Embedding ↗TODAY 04:00 UTC
arXiv cs.LGPACE: Progressive Angular-to-Norm Contrastive Embedding ↗TODAY 04:00 UTC

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