• Part of
    Ubiquity Network logo
    Interesse beim KIT-Verlag zu publizieren? Informationen für Autorinnen und Autoren

    Online lesen
  • No readable formats available
  • Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data

    Lanxiao Li

    Band 33 von Forschungsberichte aus der Industriellen Informationstechnik
     Download

    Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

    Umfang: XIII, 227 S.

    Preis: 45.00 €

    Wikipedia Concepts

    These are words or phrases in the text that have been automatically identified by the Named Entity Recognition and Disambiguation service, which provides Wikipedia () and Wikidata () links for these entities.

    Metrics:

    Empfohlene Zitierweise
    Li, L. 2024. Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000168541
    Li, L., 2024. Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000168541
    Li, L. Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data. KIT Scientific Publishing, 2024. DOI: https://doi.org/10.5445/KSP/1000168541
    Li, L. (2024). Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000168541
    Li, Lanxiao. 2024. Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000168541




    Export to:




    Lizenz

    Dieses Buch ist lizenziert unter Creative Commons Attribution + ShareAlike 4.0

    Peer Review Informationen

    Dieses Buch ist Peer reviewed. Informationen dazu finden Sie hier

    Weitere Informationen

    Veröffentlicht am 13. Mai 2024

    Sprache

    Englisch

    Seitenanzahl:

    256

    ISBN
    Paperback 978-3-7315-1346-9

    DOI
    https://doi.org/10.5445/KSP/1000168541