Scalable Active Learning for Object Detection

Elmar Haussmann; Michele Fenzi; Kashyap Chitta; Jan Ivanecky; Hanson Xu; Donna Roy; Akshita Mittel; Nicolas Koumchatzky; Clement Farabet; Jose M. Alvarez

arXiv · 2004.04699

Scalable Active Learning for Object Detection

Elmar Haussmann·Michele Fenzi·Kashyap Chitta·Jan Ivanecky·Hanson Xu·Donna Roy·Akshita Mittel·Nicolas Koumchatzky·Clement Farabet·Jose M. Alvarez

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Abstract

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.

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BibTeX RIS

Elmar Haussmann, Michele Fenzi, Kashyap Chitta, Jan Ivanecky, Hanson Xu, Donna Roy, Akshita Mittel, Nicolas Koumchatzky, Clement Farabet, Jose M. Alvarez. 2020-04-09. Scalable Active Learning for Object Detection. https://arxiv.org/abs/2004.04699

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