What is coreset selection?
Coreset selection picks a compact subset of a dataset that behaves, for the purposes of training or evaluation, much like the whole thing. Rather than sampling at random, it favors samples that are diverse and informative while dropping redundant ones, so a fraction of the data captures most of its value.
It is a practical answer to the fact that labeling and training on everything is often unnecessary and expensive.
Key takeaways
- A coreset is a small subset that approximates the full dataset.
- It keeps diverse, informative samples and drops redundant ones.
- It reduces labeling and compute cost with minimal performance loss.
How it works
Selection methods use embeddings to measure similarity and coverage, then choose points that span the dataset well, for example by maximizing diversity or coverage of the embedding space. Some approaches also weight by informativeness, such as model uncertainty. The result is a labeling or training budget spent where it counts.
Why it matters
When labeling is the bottleneck, choosing what to label matters as much as how well you label it. Coreset selection directs limited annotation budget toward the most useful samples, and it connects closely to active learning, where model feedback further sharpens the choice.
Frequently asked questions
How is coreset selection different from random sampling?
Random sampling ignores content, while coreset selection deliberately chooses diverse, informative samples that better represent the dataset.
How does it relate to active learning?
Both aim to label the most useful data. Active learning selects iteratively using model feedback, while coreset selection can be a one-shot, coverage-driven choice.
Related terms