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Few-Medoids method simplifies coreset selection for knowledge distillation

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Few-Medoids, a novel and straightforward method for coreset selection in few-shot knowledge distillation. This technique identifies representative data subsets by selecting samples closest to each class's centroid. Experiments across various image classification tasks and model architectures demonstrate that Few-Medoids consistently outperforms random selection and other coreset selection strategies. AI

IMPACT Simplifies data selection for training smaller models, potentially accelerating development and deployment.

RANK_REASON The cluster contains a research paper detailing a new method for coreset selection in machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Few-Medoids method simplifies coreset selection for knowledge distillation

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The cluster contains a research paper detailing a new method for coreset selection in machine learning.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Cemil-Andrei Dilmac, Florinel-Alin Croitoru, Radu Tudor Ionescu ·

    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-sc…

  2. arXiv cs.LG TIER_1 English(EN) · Radu Tudor Ionescu ·

    Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation

    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 networ…

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