  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.
