🤖 AI Summary
This work addresses the challenges of high computational costs and inefficient subset selection in large-scale image training. It proposes the SCOre-Stratified Selection (SCOSS) framework, which constructs a coreset through score-stratified sampling and integrates predictions from models trained on multiple independent subsets. By innovatively combining stratified sampling with ensemble learning, SCOSS significantly enhances the stability and generalization capability of coreset selection. Experimental results demonstrate that, across various sampling ratios, SCOSS-based coresets achieve state-of-the-art performance on the Simple Graph Convolution (SGC) model, surpassing support vector machines (SVMs) with only a small number of labeled samples while striking an excellent balance between accuracy and efficiency.
📝 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 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 http://scoss.lucasvalem.com.