制图主动学习
Cartography Active Learning
摘要：
我们提出了制图主动学习（CAL），一种新的主动学习（AL）算法，利用模型在训练过程中对单个实例的行为作为代理，以找到信息量最大的实例进行标记。CAL受到数据图的启发，数据图最近被提出来获得对数据集质量的见解（Swayamdipta等人，2020年）。我们将我们的方法在流行的文本分类任务上与常用的AL策略进行了比较，后者依赖于训练后的行为。我们证明了CAL是竞争力的其他常见的AL方法，表明来自小种子数据的训练动态可以成功地用于AL。我们提供了我们的新AL方法的见解，通过分析批次级统计利用数据地图。我们的研究结果进一步表明，CAL的结果在一个更有效的数据学习策略，实现相当或更好的结果与相当少的训练数据。
英文摘要：
We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
