A Coreset Selection Framework with Ensemble Aggregation for Image Classification
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.
- ▪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.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.09100 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T04:20:37.625Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.09100 (cs) [Submitted on 10 Jul 2026] Title:A Coreset Selection Framework with Ensemble Aggregation for Image Classification Authors:Pedro Rocha Dantas, Lucas Pascotti Valem View a PDF of the paper titled A Coreset Selection Framework with Ensemble Aggregation for Image Classification, by Pedro Rocha Dantas and Lucas Pascotti Valem View PDF HTML (experimental) Abstract: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.
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