论文标题
基于成本的预算积极学习进行深度学习
Cost-Based Budget Active Learning for Deep Learning
论文作者
论文摘要
主要是经典的主动学习（AL）方法通常使用统计理论，例如熵和边距来衡量实例实用程序，但是它无法捕获未标记数据中包含的数据分布信息。这最终可能导致分类器选择距离实例以标记。同时，与在典型的分类任务中误标记实例相关的损失远高于与相反误差相关的损失。为了应对这些挑战，我们提出了一个基于成本的虫子主动学习（CBAL），该学习考虑了分类不确定性以及受预算约束的人群中的实例多样性。考虑了一种基于最小值的原则方法，以最大程度地减少所选实例的标签和决策成本，这确保了近乎最理想的结果，计算工作较少。广泛的实验结果表明，所提出的方法的表现优于几种主动学习方法。
Majorly classical Active Learning (AL) approach usually uses statistical theory such as entropy and margin to measure instance utility, however it fails to capture the data distribution information contained in the unlabeled data. This can eventually cause the classifier to select outlier instances to label. Meanwhile, the loss associated with mislabeling an instance in a typical classification task is much higher than the loss associated with the opposite error. To address these challenges, we propose a Cost-Based Bugdet Active Learning (CBAL) which considers the classification uncertainty as well as instance diversity in a population constrained by a budget. A principled approach based on the min-max is considered to minimize both the labeling and decision cost of the selected instances, this ensures a near-optimal results with significantly less computational effort. Extensive experimental results show that the proposed approach outperforms several state-of -the-art active learning approaches.
