主动学习中动态选择标注级别，省钱提效
Instance-wise Supervision-level Optimization in Active Learning
- 根据价值与成本比选样本并定最优标注级别
- 相同预算下准确率更高，比现有方法低20%成本
- 适合标注资源紧张的场景，如医疗图像分析
主动学习（AL）是一种高效的机器学习范式，通过有选择地标注高价值样本提升学习效率。本文提出一种新框架——实例级监督水平优化（ISO），不仅选择需标注的样本，还确定在固定预算内每例样本的最佳标注级别。其优化标准基于各实例的价值-成本比（VCR），同时保证所选样本具有多样性。分类实验表明，ISO持续优于传统主动学习方法，并超越结合全量与弱监督的先进方法，在更低总成本下实现更高准确率。代码已开源：https://github.com/matsuo-shinnosuke/ISOAL。
原文摘要 · Abstract (English)
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness can be further enhanced by incorporating weak supervision, which uses rough yet cost-effective annotations instead of exact (i.e., full) but expensive annotations. We introduce a novel AL framework, Instance-wise Supervision-Level Optimization (ISO), which not only selects the instances to annotate but also determines their optimal annotation level within a fixed annotation budget. Its optimization criterion leverages the value-to-cost ratio (VCR) of each instance while ensuring diversity among the selected instances. In classification experiments, ISO consistently outperforms traditional AL methods and surpasses a state-of-the-art AL approach that combines full and weak supervision, achieving higher accuracy at a lower overall cost. This code is available at https://github.com/matsuo-shinnosuke/ISOAL.
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