Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
The paper introduces Statistical Flow Matching (SFM) to efficiently distill knowledge from large datasets into compact synthetic ones for pre-trained self-supervised models. SFM optimizes synthetic images by aligning statistical flows between target and non-target class centers, significantly reducing computational cost compared to previous methods like Linear Gradient Matching.