Scalable Active Learning for Object Detection
- URL: http://arxiv.org/abs/2004.04699v1
- Date: Thu, 9 Apr 2020 17:28:56 GMT
- Title: Scalable Active Learning for Object Detection
- Authors: Elmar Haussmann, Michele Fenzi, Kashyap Chitta, Jan Ivanecky, Hanson
Xu, Donna Roy, Akshita Mittel, Nicolas Koumchatzky, Clement Farabet, Jose M.
Alvarez
- Abstract summary: Active learning is a powerful technique to improve data efficiency for supervised learning methods.
We have built a scalable production system for active learning in the domain of autonomous driving.
- Score: 20.99502312184771
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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