无需训练即可高效选核心数据集，提升模型效率与抗噪能力。
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
- 利用子模函数统一覆盖与密度目标，设计无训练采样策略。
- 高删减率下性能超越有训练基线，计算开销大幅降低。
- 对标签噪声鲁棒，适合真实场景大规模应用。
核心数据集选择的目标是识别数据集中具有代表性的子集，以实现高效模型训练。然而，现有方法存在悖论：需基于整个数据集计算昂贵的训练信号（如梯度、决策边界估计或遗忘计数），在剪枝前完成，这违背了其避免训练样本的初衷。本文提出SubZeroCore，一种全新的无训练核心数据集选择方法，将子模覆盖与密度整合到单一统一目标中。为此，我们设计了一种基于闭式解的采样策略，通过单个超参数显式控制局部密度测量的期望覆盖程度。尽管无需训练，大量实验表明，SubZeroCore在性能上可媲美训练基线，在高删减率下显著超越它们，同时大幅降低计算开销。SubZeroCore还表现出对标签噪声的更强鲁棒性，凸显其在真实场景中的实用性和可扩展性。
原文摘要 · Abstract (English)
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-based signals, e.g., gradients, decision boundary estimates or forgetting counts, computed over the entire dataset prior to pruning, which undermines their very purpose by requiring training on samples they aim to avoid. We introduce SubZeroCore, a novel, training-free coreset selection method that integrates submodular coverage and density into a single, unified objective. To achieve this, we introduce a sampling strategy based on a closed-form solution to optimally balance these objectives, guided by a single hyperparameter that explicitly controls the desired coverage for local density measures. Despite no training, extensive evaluations show that SubZeroCore matches training-based baselines and significantly outperforms them at high pruning rates, while dramatically reducing computational overhead. SubZeroCore also demonstrates superior robustness to label noise, highlighting its practical effectiveness and scalability for real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。