Are Coreset Selection Methods Worth Their Cost?
Coreset selection picks a representative subset of the labeled training set to make training cheaper. However, it is usually evaluated by downstream accuracy at a fixed subset size, ignoring both the time spent selecting the subset and the training recipe behind each reported number. We introduce an end-to-end benchmark that standardizes downstream training and charges selection and training to the same auditable wall-clock budget, spanning 4 datasets from CIFAR-10 to ImageNet-1K, 11 selectors, 5 fractions, and 3 seeds, with over 1,500 released runs. Repeated-sampling work has shown that budget-aware evaluation already favors random strategies. Our two budget studies test whether that ver...
| Authors | Yangze Liu, Zhongyi Han |
|---|---|
| Primary category | cs.LG |
| Published | 19 September 2026 |
| Last updated at source | 29 September 2026 |
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