FAST：面向核心集选择的拓扑感知频域分布匹配
FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection
把核心集选择建模为谱图约束下的频域分布匹配问题，用特征函数距离（CFD）一次性约束所有阶矩，并提供无需 DNN 的 CPU 级高效实现；做视觉数据压缩、训练集蒸馏或大规模预训练数据筛选时，可直接替换随机采样或基于 DNN 的打分策略。
原文依据 the first DNN-free distribution-matching coreset selection framework that formulates coreset selection as a graph-constrained optimization problem
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论核心集选择将大规模数据集压缩为紧凑且具有代表性的子集，从而降低深度神经网络训练的能量与计算负担。现有方法要么基于 DNN，这类方法本质上与网络特定参数耦合，不可避免地引入架构偏差并损害泛化能力；要么不依赖 DNN，这类方法使用启发式规则，缺乏关于稳定性与准确性的严格理论保证。两类方法都未显式约束代表子集的分布等价性，这主要是因为连续分布匹配被普遍认为不适用于离散数据集采样。此外，常用的分布度量（如 MSE、KL、MMD 和 CE）往往无法准确刻画高阶矩差异。这些缺陷导致核心集性能次优，使所选核心集无法真正等价于原始数据集。在本工作中，我们提出 FAST（基于拓扑的频域对齐采样），这是首个不依赖 DNN 的分布匹配核心集选择框架，它将核心集选择建模为基于谱图理论的图约束优化问题，并采用特征函数距离（CFD）在频域中捕获完整的分布信息（即所有阶矩与内在相关性）。我们进一步发现，朴素的 CFD 在中高频区域存在“相位梯度消失”问题；为此，我们引入衰减相位解耦 CFD。此外，为获得更好的收敛性，我们设计了渐进式差异感知采样策略，从低频到高频逐步调度频率选择。该策略在细化局部细节之前先保留全局结构，从而能够以少量频率实现准确匹配并防止过拟合。大量实验表明，FAST 在所有评测基准上均显著优于最优的核心集选择方法，平均准确率提升 9.12%。与其他基线核心集方法相比，它将功耗降低 96.57%，即使在 CPU 上、仅用 1.7GB 内存也能实现平均 2.2 倍加速，凸显了其高性能与高能效。
英文原文摘要 2282 字符
Coreset selection compresses large datasets into compact, representative subsets, reducing the energy and computational burden of training deep neural networks. Existing methods are either: (i) DNN-based, which are inherently coupled with network-specific parameters, inevitably introducing architectural bias and compromising generalization; or (ii) DNN-free, which utilize heuristics that lack rigorous theoretical guarantees for stability and accuracy. Neither approach explicitly constrains distributional equivalence of the representative subsets, largely because continuous distribution matching is broadly considered inapplicable to discrete dataset sampling. Furthermore, prevalent distribution metrics (e.g., MSE, KL, MMD, and CE) are often incapable of accurately capturing higher-order moment differences. These deficiencies lead to suboptimal coreset performance, preventing the selected coreset from being truly equivalent to the original dataset.In this work, we propose FAST (Frequency-domain Aligned Sampling via Topology), the first DNN-free distribution-matching coreset selection framework that formulates coreset selection as a graph-constrained optimization problem grounded in spectral graph theory and employs the Characteristic Function Distance (CFD) to capture full distributional information (i.e., all moments and intrinsic correlations) in the frequency domain. We further discover that naive CFD suffers from a "vanishing phase gradient" issue in medium and high-frequency regions; to address this, we introduce an Attenuated Phase-Decoupled CFD. Furthermore, for better convergence, we design a Progressive Discrepancy-Aware Sampling strategy that progressively schedules frequency selection from low to high. This preserves global structures before refining local details, enabling accurate matching with few frequencies while preventing overfitting. Extensive experiments demonstrate that FAST significantly outperforms state-of-the-art coreset selection methods across all evaluated benchmarks, achieving an average accuracy gain of 9.12%. Compared to other baseline coreset methods, it reduces power consumption by 96.57% and achieves a 2.2xaverage speedup even on CPU with 1.7GB of memory, underscoring its high performance and energy efficiency.