• Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data 

      Choi, Changkyu; Kampffmeyer, Michael; Jenssen, Robert; Handegard, Nils Olav; Salberg, Arnt-Børre (Peer reviewed; Journal article, 2023)
      Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. ...
    • Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data 

      Choi, Changkyu; Kampffmeyer, Michael; Jenssen, Robert; Handegard, Nils Olav; Salberg, Arnt-Børre (Peer reviewed; Journal article, 2023)
      Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. ...
    • Machine Learning + Marine Science: Critical Role of Partnerships in Norway 

      Handegard, Nils Olav; Eikvil, Line; Jenssen, Robert; Kampffmeyer, Michael; Salberg, Arnt Børre; Malde, Ketil (Others, 2021)
      In this essay, we review some recent advances in developing machine learning (ML) methods for marine science applications in Norway. We focus mostly on deep learning (DL) methods and review the challenges we have faced in ...
    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Peer reviewed; Journal article, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...