Representation Alignment from a Few Paired Data via Distilled Optimal Transport

What happened

The paper introduces a representation alignment framework that leverages optimal transport with marginal constraints to align encoders using sparse paired data. The method distills alignment knowledge into compact models, enabling efficient cross-modal correspondence without requiring full modal observations for non-anchor data.

Why it matters

This approach reduces the dependency on large-scale labeled datasets for alignment tasks, which is critical for applications where paired data is scarce but unpaired data is abundant, such as in multimodal learning and cross-lingual tasks.

• Minimal paired data can achieve effective representation alignment with optimal transport
• Distillation techniques preserve alignment quality in compact models
• Hubness mitigation improves matching reliability in high-dimensional spaces
• The approach is applicable to diverse modalities like image-text and molecule-text

The bigger picture

The method enables more efficient alignment of pre-trained models in resource-constrained scenarios, potentially improving performance in domains with limited labeled data while maintaining scalability through unpaired data utilization.

The paper demonstrates a novel alignment method that significantly reduces the need for large paired datasets while maintaining strong performance, offering a practical solution for cross-modal learning with sparse supervision.

Technical details

• Optimal transport with entropic regularization for point cloud matching
• Hubness mitigation through marginal constraints
• Distillation of alignment plans into lightweight encoders
• Anchor-based representation encoding using pairwise similarities

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