精准生成填补数据空白的合成样本，提升模型性能。
LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
- 结合边界距离与不确定性，筛选高价值合成数据。
- 在稀疏区域生成有效样本，准确率超越传统方法。
- 适合数据稀缺场景下的小样本学习与增量训练。
合成数据的有效性取决于其能否填补下游任务关键的训练分布空白。本文提出LiBaGS，一种轻量级、无需依赖生成器的目标化合成数据选择方法。通过融合决策边界邻近度、预测不确定性、真实数据密度及支持有效性对候选样本评分，确保所选样本既具信息量又位于真实数据流形上。采用边界间隙分配策略，聚焦稀疏但真实的决策边界邻域，而非简单增加数据或仅选最不确定样本。此外，引入边际价值停止规则判断何时停止添加合成样本，在模糊边界处赋予软标签，并使用多样性目标避免重复选择。实验表明，LiBaGS在准确率上优于经典过采样、硬增强、不确定性与密度消融实验，以及目标生成选择标准。
原文摘要 · Abstract (English)
Synthetic data is useful only when the added samples fill missing parts of the training distribution that matter for the downstream task. We introduce LiBaGS, a lightweight, generator-agnostic method for targeted synthetic training data selection. LiBaGS scores candidate synthetic samples by combining decision-boundary proximity, predictive uncertainty, real-data density, and support validity, so that selected samples are both informative and likely to remain on the real data manifold. We then use a boundary-gap allocation rule that targets sparse but realistic decision-boundary neighborhoods, rather than simply adding more data or selecting only the most uncertain candidates. LiBaGS also learns when enough synthetic samples have been added through a marginal-value stopping rule, assigns softer labels near ambiguous boundaries, and uses a diversity objective to avoid redundant near-duplicate selections. Experiments show that LiBaGS improves accuracy over classical oversampling, hard augmentation, uncertainty and density ablations, and targeted-generation selection criteria.
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