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openai.com·13h ago
AI Research
arXiv CS.AI·May 25·8 min read
Multimodal Distribution Matching for Vision-Language Dataset Distillation
TL;DR
Training vision-language models (systems that process both images and text) requires massive datasets, but compressing them into smaller synthetic versions while maintaining performance is difficult because prior methods ignore how images and text relate. Multimodal Distribution Matching (MDM) addresses this by matching statistical distributions across both modalities simultaneously using geometry-aware techniques at data, model, and loss levels.
✦ Why It Matters
Engineers can now compress vision-language training data efficiently while maintaining cross-modal alignment and reducing training costs across different model architectures.
Key Takeaways
- MDM reduces distillation cost while preserving image-text semantic relationships in compact synthetic datasets.
- Initialize synthetic pairs from joint embedding clusters to maintain multimodal structure from the start.
- Use mixed teacher models interpolated by angular deviation to capture diverse knowledge efficiently.
- Apply geometry-aware matching on unit hypersphere with symmetric contrastive learning for robust cross-modal agreement.
Full Summary
Modern AI systems increasingly process paired vision-language inputs (images with captions), but training on massive datasets is expensive. Dataset distillation compresses large training sets into smaller synthetic versions while preserving performance—a critical challenge for deployment.
Prior multimodal distillation methods required heavy computation and overlooked correlations between image and text modalities. Multimodal Distribution Matching (MDM) is a geometry-aware framework that operates at three levels: initializing synthetic image-text pairs from clusters in joint embedding space (the mathematical representation where images and text coexist), creating mixed teacher models by interpolating fine-tuned weights based on angular deviation, and matching distributions on a unit hypersphere using symmetric contrastive learning.
Evaluated on image-text retrieval benchmarks with cross-architecture testing, MDM generated compact synthetic datasets that preserved multimodal semantics, substantially reduced distillation computational cost, and remained robust when transferred to different model architectures.