Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data. However, its effectiveness diminishes significantly when the labeling budget is low. In this paper, we first empirically observe the performance degradation of existing AL algorithms in the low-budget settings, and then introduce Direct Acquisition Optimization (DAO), a novel AL algorithm that optimizes sample selections based on expected true loss reduction. Specifically, DAO utilizes influence functions to update model parameters and incorporates an additional acquisition strategy to mitigate bias in loss estimation. This approach facilitates a more accurate estimation of the overall error reduction, without extensive computations or reliance on labeled data. Experiments demonstrate DAO's effectiveness in low budget settings, outperforming state-of-the-arts approaches across seven benchmarks.

在本文中，我们首先通过实证观察到现有的主动学习算法在低预算环境下的性能下降，然后介绍了一种名为直接获取优化（DAO）的新型主动学习算法，该算法基于期望真实损失减少来优化样本选择。具体而言，DAO利用影响函数来更新模型参数，并结合了一种附加的获取策略来减小损失估计中的偏差。该方法能够更准确地估计整体错误减少，而无需进行大量计算或依赖标记数据。实验结果表明，在低预算环境下，DAO在七项基准测试中超越了现有方法。

低预算主动学习的直接获取优化