A Coreset Selection Framework with Ensemble Aggregation for Image Classification
Resumo
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 sampled training subset. As baselines, we use moderate and random selection, each in original and class-balanced versions. We assess the framework with Simple Graph Convolution (SGC) and Support Vector Machine (SVM) classifiers under different sampling ratios. Experiments show that SCOSS is competitive with baselines, often the best choice for SGC, and enables favorable trade-offs between accuracy and efficiency. On the fine-grained dataset, SGC with SCOSS outperforms SVMs when using fewer labeled samples. The code and supplementary materials are publicly available at scoss.lucasvalem.com.
Referências
Gkarmpounis, G., Vranis, C., Vretos, N., and Daras, P. (2024). Survey on graph neural networks. IEEE Access, 12:128816–128832.
Guo, C., Zhao, B., and Bai, Y. (2022). Deepcore: A comprehensive library for coreset selection in deep learning. In Database and Expert Systems Applications: 33rd International Conference, DEXA 2022, Vienna, Austria, August 22–24, 2022, Proceedings, Part I, page 181–195, Berlin, Heidelberg. Springer-Verlag.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny images. Technical Report 0, University of Toronto, Toronto, Ontario.
Moser, B. B., Shanbhag, A. S., Frolov, S., Raue, F., Folz, J., and Dengel, A. (2026). A coreset selection of coreset selection literature: Introduction and recent advances. arXiv preprint arXiv:2505.17799.
Sagi, O. and Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4):e1249.
Tsai, E., Zheng, H., and Prakash, A. (2025). Class-proportional coreset selection for difficulty-separable data. arXiv preprint arXiv:2507.10904.
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011). The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology.
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. (2019). Simplifying graph convolutional networks. In Chaudhuri, K. and Salakhutdinov, R., editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 6861–6871. PMLR.
Xia, X., Liu, J., Yu, J., Shen, X., Han, B., and Liu, T. (2023). Moderate coreset: A universal method of data selection for real-world data-efficient deep learning. In ICLR.
Zheng, H., Liu, R., Lai, F., and Prakash, A. (2023). Coverage-centric coreset selection for high pruning rates. arXiv preprint arXiv:2210.15809.
