• Bokmål
  • English

Sitemap

Deep Learning for Applications

Deep Learning for Applications

After 2012 when deep learning based techniques won the ImageNet contest with a clear margin to competing algorithms, deep learning has been called “the revolutionary technique that quietly changed machine vision forever”. For many classification tasks deep learning has drastically surpassed previous state of the art results in classification accuracy. Currently large deep neural networks achieve the best results on speech recognition, visual object recognition, character recognition, and several language related tasks.

The power of deep learning

Deeper machine learning architectures are better capable of handling complex recognition tasks compared to previous more shallow models. Major benefits of deep networks are:

  • their superior modeling capabilities of heterogeneous data in layers of increasing complexity,
  • their ability to learn the best features to represent the raw data, and
  • their ability to gain in performance with the availability of more training data.
     

Specialized deep learning

The next wave of vision technology will take place for other applications than traditional computer vision, such as medical imaging, marine and seismic imaging, remote sensing of ocean, land and infrastructure, process monitoring and industry. These applications depend upon non-standard imagery and present challenges that needs to be solved in order to benefit from the untapped potential:

  • Learning from limited data sets
  • Transferring knowledge across domains
  • Exploiting non-standard and heterogeneous imagery
  • Capturing context and dependencies
  • Quantification of uncertainties in predictions
  • Reliable and explainable predictions
     

Combining years of experience in image analysis and machine learning

The image analysis and machine learning group at NR and the machine learning group at UiT work together to better understand the needs and to develop state-of-the-art specialized deep learning solutions suitable for solving specific problems for various industry-, medical and environmental applications.

Current activities and projects involving deep learning
 

    Norwegian Cancer Registry

    MIM
    Norwegian Cancer Registry, partly funded by the Research Council of Norway
    Use of deep learning and Big Data in the Norwegian Breast Cancer Screening Program

     

    DELI
    Equinor
    Deep learning for seismic interpretation

    COGSAT

    COGSAT

    European Space Agency
    Automatic analysis of Sentinel-data using deep learning techniques

    COGMAR
    IKTpluss, Research Council of Norway
    Automatic analysis of marine data using deep learning techniques.

    InfraUAS

    Orbiton AS, partly funded by the Research Council of Norway, BIA programme

    Monitoring of critical infrastructure using UAVs

    INCUS
    GE Vingmed Ultrasound, partly funded by the Research Council of Norway, BIA programme
    Intelligent Cardiovascular Ultrasound Scanner

    Hyperbio

    TerraTec, partly funded by the Research Council of Norway, BIA programme

    Automatic mapping of forest species using deep learning

    AIRQUIP
    Research Council of Norway
    Automatic estimation of traffic from VHR satellite images using deep learning techniques.

    HBR
    Infrastructure programme, Research Council of Norway
    Transcription of historical Norwegian census forms

    UAVSEAL
    Institute of Marine Research, funded by the Research Council of Norway

    Detection and counting of seals on ice from aerial images

    LASTRAK
    Norwegian Mapping Authority
    Tracking of small roads and forest paths from laser data

    CULTSEARCHER
    Norwegian Directorate for Cultural Heritage
    Detection of cultural heritage sites from laser data

    SNOWBALL
    EEA grants
    Automatic detection and mapping of avalanches in optical and SAR satellite images.

     

    Postal address:
    Norsk Regnesentral/
    Norwegian Computing Center
    P.O. Box 114 Blindern
    NO-0314 Oslo
    Norway
    Visit address:
    Norsk Regnesentral
    Gaustadalleen 23a
    Kristen Nygaards hus
    NO-0373 Oslo.
    Phone:
    (+47) 22 85 25 00
    Address How to get to NR
    Social media Share on social media
    Privacy policy Privacy policy
    Postal address: Norsk Regnesentral/Norwegian Computing Center, P.O. Box 114 Blindern, NO-0314 Oslo, Norway
    Visit address: Norsk Regnesentral, Gaustadalleen 23a, Kristen Nygaards hus, NO-0373 Oslo.
    Phone: (+47) 22 85 25 00
    AddressHow to get to NR