# STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings Source: [STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:23:26.146Z Authors: Nathan Beck, Suraj Kothawade, Pradeep Shenoy, Rishabh Iyer Published: 2023-05-18T02:01:45.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()