  Deep neural networks have consistently shown great performance in several
real-world use cases like autonomous vehicles, satellite imaging, etc.,
effectively leveraging large corpora of labeled training data. However,
learning unbiased models depends on building a dataset that is representative
of a diverse range of realistic scenarios for a given task. This is challenging
in many settings where data comes from high-volume streams, with each scenario
occurring in random interleaved episodes at varying frequencies. We study
realistic streaming settings where data instances arrive in and are sampled
from an episodic multi-distributional data stream. Using submodular information
measures, we propose STREAMLINE, a novel streaming active learning framework
that mitigates scenario-driven slice imbalance in the working labeled data via
a three-step procedure of slice identification, slice-aware budgeting, and data
selection. We extensively evaluate STREAMLINE on real-world streaming scenarios
for image classification and object detection tasks. We observe that STREAMLINE
improves the performance on infrequent yet critical slices of the data over
current baselines by up to $5\%$ in terms of accuracy on our image
classification tasks and by up to $8\%$ in terms of mAP on our object detection
tasks.
