Title: Bridging between Pool- and Stream-Based Active Learning with Temporal Data Coherence. Abstract: Active learning (AL) reduces the amount of labeled data needed for training a machine learning model by choosing intelligently which instances to label. Classic
pool-based AL needs 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 it ever reaches the datacenter. In our work, we
investigate AL for such image streams and propose a new concept exploiting their
temporal properties. We define three methods using a pseudo uncertainty based
on loss learning (Yoo & Kweon, 2019). The first considers the temporal change
of uncertainty and requires 5% less labeled data than the vanilla approach. It is
extended by the change in latent space in the second method. The third method,
temporal distance loss stream (TDLS), combines both with submodular optimization. In our evaluation on an extension of the public Audi Autonomous Driving
Dataset (Geyer et al., 2020) we outperform state-of-the-art approaches by using
1% fewer labels. Additionally, we compare our stream-based approaches with
existing approaches for AL in a pool-based scenario. Our experiments show that,
although pool-based AL has access to more data, our stream-based AL approaches
need 0.5% fewer labels.