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- W3120338137 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." @default.
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- W3120338137 date "2020-10-19" @default.
- W3120338137 modified "2023-10-18" @default.
- W3120338137 title "Scalable Active Learning for Object Detection" @default.
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- W3120338137 doi "https://doi.org/10.1109/iv47402.2020.9304793" @default.
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