  Active learning, a powerful paradigm in machine learning, aims at reducing
labeling costs by selecting the most informative samples from an unlabeled
dataset. However, the traditional active learning process often demands
extensive computational resources, hindering scalability and efficiency. In
this paper, we address this critical issue by presenting a novel method
designed to alleviate the computational burden associated with active learning
on massive datasets. To achieve this goal, we introduce a simple, yet effective
method-agnostic framework that outlines how to strategically choose and
annotate data points, optimizing the process for efficiency while maintaining
model performance. Through case studies, we demonstrate the effectiveness of
our proposed method in reducing computational costs while maintaining or, in
some cases, even surpassing baseline model outcomes. Code is available at
https://github.com/aimotive/Compute-Efficient-Active-Learning.
