Skip to content
Back Home

Centre for Robotics & Vision @ AUT

Centre for Robotics & Vision @ AUT

  • People
    • Staff & PhD Students
    • Alumni & Collaborators
    • Visitors
  • Publication
    • 2022 publication
    • 2021 Publication
    • 2020 Publication
    • 2019 Publication
    • 2018 Publication
    • 2017 Publication
    • 2016 Publication
    • 2015 Publication
    • 2014 Publication
    • 2013 Publication
    • 2012 Publication
    • All Publication
  • Seminars & Conferences
    • CeRV Conferences
    • Seminars
      • 2022 Seminars
      • 2021 Seminars
      • 2020 Seminars
      • 2019 Seminars
      • 2018 Seminars
      • 2017 Seminars
      • 2016 Seminars
      • 2015 Seminars
    • Conferences
      • 2022 Conferences
      • 2021 Conferences
      • 2020 Conferences
      • 2019 Conferences
      • 2018 Conferences
      • 2017 Conferences
      • 2016 Conferences
      • 2015 Conferences
    • Invited Talks
    • Keynotes
  • Awards/Recognition
  • Data & TR’S
    • Technical Reports
      • 2022 Technical Reports
      • 2021 Technical Reports
      • 2020 Technical Reports
      • 2019 Technical Reports
      • 2018 Technical Reports
      • 2017 Technical Reports
      • 2016 Technical Reports
      • 2015 Technical Reports
      • 2014 Technical Reports
      • 2013 Technical Reports
      • 2012 Technical Reports
      • 2011 Technical Reports
      • 2010 Technical Reports
      • 2009 Technical Reports
      • 2008 Technical Reports
      • 2007 Technical Reports
    • EISATS
      • Set 11
      • Set 10
      • Set 9
      • Set 8
      • Set 7
      • Set 6
      • Set 5
      • Set 4
      • Set 3
        • Sources
          • Lectures
        • Object Detection
      • Set 1
        • Lectures & Talks
          • Invited Talks
            • Set 2
    • Vehicle Datasets
    • Media
      • Images
      • Videos
  • News
    • 2022 News
    • 2021 News
    • 2020 News
    • 2019 News
    • 2018 News
    • 2017 News
    • 2016 News
    • 2015 News
Home » Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic