DISSect selects training samples for multimodal contrastive learning by ranking the difference between historical and current model similarity scores, matching full-data performance with 70% fewer samples.
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Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning
DISSect selects training samples for multimodal contrastive learning by ranking the difference between historical and current model similarity scores, matching full-data performance with 70% fewer samples.