论文标题
部分：多任务视觉设置中有效的部分主动学习
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings
论文作者
论文摘要
多任务学习对于许多现实世界应用都是核心。不幸的是，获得所有任务的标签数据是耗时，具有挑战性且昂贵的。积极学习（AL）可用于减轻这种负担。现有技术通常涉及选择要注释的图像，并为所有任务提供注释。 在本文中，我们表明，不仅选择要注释的图像，还可以在每种迭代时提供注释的一部分任务。此外，所提供的注释可用于猜测伪标记的伪标签，以供保持未经注释的任务。我们证明了方法对几个流行的多任务数据集的有效性。
Multi-task learning is central to many real-world applications. Unfortunately, obtaining labelled data for all tasks is time-consuming, challenging, and expensive. Active Learning (AL) can be used to reduce this burden. Existing techniques typically involve picking images to be annotated and providing annotations for all tasks. In this paper, we show that it is more effective to select not only the images to be annotated but also a subset of tasks for which to provide annotations at each AL iteration. Furthermore, the annotations that are provided can be used to guess pseudo-labels for the tasks that remain unannotated. We demonstrate the effectiveness of our approach on several popular multi-task datasets.
