Selecting per-class medoids (samples with lowest average L2 distance to all same-class samples in teacher feature space) consistently outperforms random, herding, and k-center Greedy baselines for few-shot knowledge distillation on from-scratch students.
Weight copy and low-rank adaptation for few-shot distillation of vision transformers, in: Proceedings of W ACV , pp
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Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
Selecting per-class medoids (samples with lowest average L2 distance to all same-class samples in teacher feature space) consistently outperforms random, herding, and k-center Greedy baselines for few-shot knowledge distillation on from-scratch students.