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.

本文提出了一种基于子模信息度量的新型流式活动学习框架STREAMLINE，该框架通过识别切片、切片感知预算和数据选择的三步过程来补偿工作标记数据中情节驱动的切片不平衡，并在真实世界的流式场景中对图像分类和目标检测任务进行了广泛评估，发现相比基线方法STREAMLINE在数据的不频繁但关键的情节上的性能提高了5％的分类准确度和8％的mAP。

流式主动学习：针对现实多分布环境的流式学习