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.
- arXiv
- Coreset selection
- Few-Medoids
- herd behavior
- image classification
- k-center Greedy
- knowledge distillation
- transformer networks
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