Scalable Active Learning for Object Detection
This work addresses the problem of reducing annotation costs for autonomous driving companies, but it appears incremental as it focuses on scaling existing active learning methods.
The paper tackles the challenge of selecting the most informative data for labeling in autonomous driving by developing a scalable active learning system, resulting in improved data efficiency for object detection.
Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled data is already a major undertaking, only a subset of it can be labeled by humans due to the effort needed for high-quality annotation. Therefore, finding the right data to label has become a key challenge. Active learning is a powerful technique to improve data efficiency for supervised learning methods, as it aims at selecting the smallest possible training set to reach a required performance. We have built a scalable production system for active learning in the domain of autonomous driving. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, present our current results at scale, and briefly describe the open problems and future directions.