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.
We address these challenges with a framework that combines coreset selection with 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 Resource.
Blogger's Review: This study demonstrates the effectiveness of combining coreset selection with ensemble learning, especially for large-scale image data. The SCOSS method maintains good classification performance while improving model efficiency, providing new insights and methods for future research. The SGC performance is particularly impressive when samples are limited, making it a noteworthy approach.