提出基于梯度差异的主动学习选样方法，提升数据筛选效率。
Gradient-Discrepancy Acquisition for Pool-Based Active Learning
- 利用梯度差异设计新选样准则，替代传统不确定性度量。
- 在多种任务中表现优于现有方法，显著降低标注成本。
- 适合需要高效标注的场景，如数据稀缺或标注昂贵任务。
主动学习的效果取决于学习算法选择有信息量的数据点进行标注的获取准则。本文提出一种新的基于梯度的获取准则，该准则源自Luo等人（2022）提出的泛化界。该准则可替代不确定度采样中的不确定性度量，也可融入考虑样本分布多样性的方法中。我们为所提准则提供了理论依据，并通过实证评估验证了其有效性。
原文摘要 · Abstract (English)
The effectiveness of active learning hinges on the choice of the acquisition criterion by which a learning algorithm selects potentially informative data points whose label is subsequently queried. This paper proposes a novel gradient-based acquisition criterion, derived from a generalization bound introduced by Luo et al. (2022). This criterion can be applied in lieu of uncertainty measures in uncertainty sampling, or incorporated into diversity-based methods that consider the spread of sampled points in addition to the uncertainty of their labels. We provide a theoretical justification of the proposed acquisition criterion, and demonstrate its effectiveness in an empirical evaluation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。