Active learning (AL) reduces the amount of labeled data needed to train a machine learning model by intelligently choosing which instances to label. Classic pool-based AL requires all data to be present in a datacenter, which can be challenging with the increasing amounts of data needed in deep learning. However, AL on mobile devices and robots, like autonomous cars, can filter the data from perception sensor streams before reaching the datacenter. We exploited the temporal properties for such image streams in our work and proposed the novel temporal predicted loss (TPL) method. To evaluate the stream-based setting properly, we introduced the GTA V streets and the A2D2 streets dataset and made both publicly available. Our experiments showed that our approach significantly improves the diversity of the selection while being an uncertainty-based method. As pool-based approaches are more common in perception applications, we derived a concept for comparing pool-based and stream-based AL, where TPL out-performed state-of-the-art pool- or stream-based approaches for different models. TPL demonstrated a gain of 2.5 precept points (pp) less required data while being significantly faster than pool-based methods.

翻译：主动学习（AL）通过智能选择需要标注的样本来减少训练机器学习模型所需的标注数据量。经典的基于池的主动学习要求所有数据集中存储在数据中心，但随着深度学习所需数据量的不断增加，这一要求变得颇具挑战性。然而，移动设备和机器人（如自动驾驶汽车）上的主动学习可以在感知传感器流数据到达数据中心之前对其进行过滤。本研究利用此类图像流的时间属性，提出了一种新颖的时序预测损失（TPL）方法。为正确评估基于流的设置，我们引入了GTA V街道数据集和A2D2街道数据集，并将其公开。实验表明，我们的方法作为一种基于不确定性的方法，显著提升了选择的多样性。由于基于池的方法在感知应用中更为常见，我们推导了一种比较基于池与基于流的主动学习的概念框架，其中TPL在不同模型上均优于最先进的基于池或基于流的方法。TPL在所需数据量减少2.5个百分点（pp）的同时，速度显著快于基于池的方法。