A Coreset Selection Framework with Ensemble Aggregation for Image Classification
The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training. Selecting representative training subsets, however, remains challenging: individual sample contributions are unclear, and model behavior varies across datasets and runs. We address these challenges with a framework that combines coreset selection with an ensemble aggregation over multiple runs. For coreset selection, we propose SCOre-Stratified Selection (SCOSS), which partitions the training data into intervals based on a chosen score and samples from each interval. The ensemble combines predictions from multiple runs, each performed on an independently sa...
| Authors | Pedro Rocha Dantas, Lucas Pascotti Valem |
|---|---|
| Primary category | cs.CV |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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