发表机构
Shandong University; Nanjing University（山东大学; 南京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过冻结子集实验证明，核心集选择中易先与覆盖标准的交叉边界随学习器结构变化，而非仅由数据决定，并识别出影响边界的网络因素，强调策略评估需结合目标学习器。
AI 中文摘要
模型需要什么样的数据才能学习？核心集选择使这个问题变得具体：在预算限制下，保留对训练最有用的样本。先易后难和几何覆盖标准在不同预算区间内可能各自胜出，二者之间由交叉边界分隔。我们探究这一边界是由数据固定不变，还是随目标学习器而变化。受控实验冻结所选子集，仅操纵训练学习器。在低分辨率ImageNet-100上，将ResNet-18的宽度加倍会使交叉点从每类57个样本移至85个样本：学习器改变了相同样本的相对价值。更广泛的扫描揭示了输入网格与容量之间的相互作用。在保留相同图像信息的同时扩大网格会使边界左移，且这种移动随宽度增加而减弱。步长控制在不改变输入网格的情况下重现并反转网格效应；仅移除最后一个下采样步长就足以恢复左移。在原生224像素的ImageNet-1k协议下，宽度效应较小且依赖于探针：LFrac保持几乎平坦，而EL2N适度右移。将卷积学习系统换成ViT会使覆盖标准在整个测量范围内胜出，即使易子集来自卷积代理。这些结果通过冻结子集干预确立了学习器依赖性，并识别出能移动边界的网络结构。它们并未产生通用缩放定律。其实践意义直接：选择策略的偏好预算区间必须相对于目标学习器进行评估。
英文摘要
What kind of data does a model need in order to learn? Coreset selection makes this question concrete: under a budget, keep the samples most useful for training. Easy-first and geometric coverage criteria can win in different budget regimes, separated by a crossover boundary. We ask whether this boundary is fixed by the data or changes with the target learner. Controlled experiments freeze the selected subsets and manipulate only the training learner. On low-resolution ImageNet-100, doubling ResNet-18's width moves the crossover from 57 to 85 samples per class: the learner changes the relative value of the same samples. A wider sweep reveals an interaction between input grid and capacity. Enlarging the grid while retaining the same image information shifts the boundary left, and this shift weakens as width increases. Stride controls reproduce and reverse the grid effect without changing the input grid; removing only the last downsampling stride is sufficient to recover the leftward shift. Under the native-224px ImageNet-1k protocol, width effects are smaller and depend on the probe: LFrac remains nearly flat, while EL2N shifts modestly right. Swapping the convolutional learning system for a ViT makes coverage win throughout the measured range, even when the easy subsets come from the convolutional proxy. These results establish learner dependence through frozen-subset interventions and identify network structure that can move the boundary. They do not yield a universal scaling law. Their practical implication is direct: a selection strategy's preferred budget regime must be evaluated with respect to the target learner.
Comments21 pages
