Abstract:

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 in-
creasing 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 evalu-
ate 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 ap-
proach 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.

[A2D2 streets] [GTA V streets]  [BMVC 2023]

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