研究双类不平衡下主动学习的效率，发现熵与核心集策略仍有效。
When Imbalance Comes Twice: Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation
- 通过模拟缺陷分布不均与存储导致的标签漂移，测试主动学习策略。
- 在强类不平衡下，熵与核心集策略仍保持高效，但标签漂移显著降低性能。
- 适合关注工业质检、医疗图像等高成本标注场景的研究者。
主动学习旨在从大量未标注数据中选取最具信息量的样本，适用于数据量大且标注成本高的场景，如机器视觉或医学影像。机器视觉存在两个特点：一是多数图像无缺陷，二是图像数量庞大难以全部存储。这导致缺陷分布严重不均衡，同时受限于存储能力可能引发标签漂移。为探究这两种不平衡对主动学习的影响，本文基于两个开源数据集开展仿真研究，人工构建具有可控类不平衡与标签漂移水平的数据集。对比三种标准主动学习选择策略：随机采样、基于熵的选样和核心集选样。结果表明，即使在高度不平衡数据下，熵与核心集选样策略依然有效；同时量化了强标签漂移导致的效率下降。实验验证了现有方法在复杂现实条件下的鲁棒性边界。
原文摘要 · Abstract (English)
The aim of Active Learning is to select the most informative samples from an unlabelled set of data. This is useful in cases where the amount of data is large and labelling is expensive, such as in machine vision or medical imaging. Two particularities of machine vision are first, that most of the images produced are free of defects, and second, that the amount of images produced is so big that we cannot store all acquired images. This results, on the one hand, in a strong class imbalance in defect distribution and, on the other hand, in a potential label shift caused by limited storage. To understand how these two forms of imbalance affect active learning algorithms, we propose a simulation study based on two open-source datasets. We artificially create datasets for which we control the levels of class imbalance and label shift. Three standard active learning selection strategies are compared: random sampling, entropy-based selection, and core-set selection. We demonstrate that active learning strategies, and in particular the entropy-based and core-set selections, remain interesting and efficient even for highly imbalanced datasets. We also illustrate and measure the loss of efficiency that occurs in the situation a strong label shift.
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