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：主动学习通过智能选择需要标注的实例，能够减少训练机器学习模型所需的标注数据量。传统的基于池的主动学习要求所有数据都存在于数据中心，但随着深度学习所需数据量的不断增加，这种方式面临挑战。然而，在移动设备和机器人（如自动驾驶汽车）上进行主动学习时，可以在数据到达数据中心之前过滤来自感知传感器流的数据。我们在工作中利用了此类图像流的时序属性，并提出了一种新颖的时序预测损失方法。为了正确评估基于流的环境，我们引入了GTA V街道和A2D2街道数据集，并将两者公开提供。我们的实验表明，虽然该方法基于不确定性，但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，我们推导出了一个用于比较基于池和基于流的主动学习的概念框架，在该框架中，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表现出所需数据减少2.5个百分点，同时速度显著快于基于池的方法。