Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
arXiv:2607.05891v1 Announce Type: cross Abstract: Coreset selection aims to identify a small and highly representative subset of a massive dataset for efficient model training. The problem remains challenging even in the few-shot knowledge distillation (KD) setup, where a full-scale pre-trained teacher informs the student network. Typical sample selection strategies often struggle to surpass the random selection baseline. In this paper, we showcase few-medoids, an embarrassingly simple coreset selection strategy that chooses the samples closest to the centroid (average image) of each class. We
The continuous growth of massive datasets in AI training necessitates more efficient coreset selection methods to keep training times and computational costs manageable.
This development proposes a simple yet effective method for few-shot knowledge distillation, potentially accelerating model training and reducing resource requirements for developing AI systems.
The conventional struggle with sample selection strategies in few-shot knowledge distillation might be overcome by a straightforward 'few-medoids' approach, making model efficiency more accessible.
- · AI researchers
- · ML model developers
- · Cloud computing providers (reduced egress/compute)
- · Companies with large datasets
- · Inefficient coreset selection methods
- · Hardware vendors (if efficiency reduces demand)
Faster and cheaper development of specialized AI models.
Democratization of advanced AI capabilities by lowering computational barriers for smaller teams.
Increased proliferation of AI applications as training becomes more efficient and less resource-intensive.
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Read at arXiv cs.AI