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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    Titel :

    Scalable Active Learning for Object Detection


    Beteiligte:
    Haussmann, Elmar (Autor:in) / Fenzi, Michele (Autor:in) / Chitta, Kashyap (Autor:in) / Ivanecky, Jan (Autor:in) / Xu, Hanson (Autor:in) / Roy, Donna (Autor:in) / Mittel, Akshita (Autor:in) / Koumchatzky, Nicolas (Autor:in) / Farabet, Clement (Autor:in) / Alvarez, Jose M. (Autor:in)


    Erscheinungsdatum :

    19.10.2020


    Format / Umfang :

    3312748 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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