通过候选集查询降低分类主动学习的标注成本
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
- 用置信区间动态生成小而可靠的候选类别集，缩小标注范围
- 在ImageNet64x64上实现48%的标注成本降低
- 适合标注资源有限、需高效筛选数据的场景
本文提出一种面向分类任务的高效主动学习框架，引入新型查询机制——候选集查询。与传统方法要求标注者遍历所有类别不同，该方法通过置信性预测动态生成包含真实类别的小规模候选集，显著缩小搜索空间并降低标注成本。同时，设计了一种兼顾信息增益与成本的获取函数，在多轮主动学习中持续优化。在CIFAR-10、CIFAR-100和ImageNet64x64上的实验表明，该框架具有良好的有效性和可扩展性，尤其在ImageNet64x64上实现48%的标注成本下降。
原文摘要 · Abstract (English)
This paper introduces a cost-efficient active learning (AL) framework for classification, featuring a novel query design called candidate set query. Unlike traditional AL queries requiring the oracle to examine all possible classes, our method narrows down the set of candidate classes likely to include the ground-truth class, significantly reducing the search space and labeling cost. Moreover, we leverage conformal prediction to dynamically generate small yet reliable candidate sets, adapting to model enhancement over successive AL rounds. To this end, we introduce an acquisition function designed to prioritize data points that offer high information gain at lower cost. Empirical evaluations on CIFAR-10, CIFAR-100, and ImageNet64x64 demonstrate the effectiveness and scalability of our framework. Notably, it reduces labeling cost by 48% on ImageNet64x64. The project page can be found at https://yehogwon.github.io/csq-al.
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