HyperCore在噪声数据中自适应选择核心数据集，提升训练效率与鲁棒性。
HyperCore: Coreset Selection under Noise via Hypersphere Models
- 基于类内超球模型，通过距离判断样本归属
- 无需调参自动确定剪枝阈值，显著提升噪声下性能
- 适合低数据量、标注不准确的现实场景
核心数据集选择的目标是从数据集中识别出能代表整体的子集，以实现高效模型训练。然而，现有方法常忽略标注错误的存在，且需固定剪枝比例，在真实场景中实用性不足。本文提出HyperCore，一种专为噪声环境设计的鲁棒且自适应的核心数据集选择框架。HyperCore利用轻量级超球模型，按类别学习，将类内样本聚集在超球中心附近，同时根据距离自然分离类外样本。通过使用Youden's J统计量，HyperCore可自适应选择剪枝阈值，实现无需调参的噪声感知数据裁剪。实验表明，相较于现有最先进方法，HyperCore在噪声环境和低数据条件下均表现更优。它能有效剔除误标和模糊样本，生成紧凑且高度信息性的子集，适用于可扩展、无噪声的学习任务。
原文摘要 · Abstract (English)
The goal of coreset selection methods is to identify representative subsets of datasets for efficient model training. Yet, existing methods often ignore the possibility of annotation errors and require fixed pruning ratios, making them impractical in real-world settings. We present HyperCore, a robust and adaptive coreset selection framework designed explicitly for noisy environments. HyperCore leverages lightweight hypersphere models learned per class, embedding in-class samples close to a hypersphere center while naturally segregating out-of-class samples based on their distance. By using Youden's J statistic, HyperCore can adaptively select pruning thresholds, enabling automatic, noise-aware data pruning without hyperparameter tuning. Our experiments reveal that HyperCore consistently surpasses state-of-the-art coreset selection methods, especially under noisy and low-data regimes. HyperCore effectively discards mislabeled and ambiguous points, yielding compact yet highly informative subsets suitable for scalable and noise-free learning.
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