发表机构
Institute of Mathematics and Computer Science (ICMC); University of São Paulo (USP); São Carlos -- SP -- Brazil（数学与计算机科学研究所; 圣保罗大学; 巴西圣保罗州萨昂卡里斯）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像数据增长带来的训练挑战，提出结合核心集选择与集成聚合的框架并使用SCOSS方法，经实验对比不同采样率下多种分类器，表明该方法在准确性和效率权衡上有优势，尤其在细粒度数据集上表现突出。
AI 中文摘要
图像数据的快速增长产生了大规模数据集，引发了对模型训练时间和内存成本的担忧。选择有代表性的训练子集具有挑战性，因为单个样本贡献不明，且模型行为因数据集和运行而异。我们提出一个框架，将核心集选择与多次运行的集成聚合相结合。对于核心集选择，我们提出分数分层选择（SCOSS），根据选定分数将训练数据划分为区间并从每个区间采样。集成聚合结合多次运行的预测，每次运行在独立采样的训练子集上进行。我们使用适度和随机选择作为基线，包括原始版本和类平衡版本。我们在不同采样率下用简单图卷积（SGC）和支持向量机（SVM）分类器评估该框架。实验表明，SCOSS与基线具有竞争力，通常是SGC的最佳选择，能在准确性和效率之间实现良好权衡。在细粒度数据集上，使用较少标记样本时，带SCOSS的SGC优于SVM。代码和补充材料可在指定网址公开获取。
英文摘要
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.
