Changkyu Choi introducing his Thesis.
Image:
Harald Lykke Joakimsen

Changkyu Choi introducing his Thesis.

High Quality PhD defense in the Visual Intelligence research centre

Changkyu Choi defended his PhD thesis “Advancing Deep Learning for Marine Environment Monitoring” on June 9th 2023 at UiT The Arctic University of Norway.

High Quality PhD defense in the Visual Intelligence research centre

The key objectives of Changkyu Choi thesis is advancing both deep learning and marine environment monitoring, this by addressing the challenges linked to limited annotated data. Choi has develop a new explainable deep learning method that generates explanations tailored to the needs and preferences of the  users and evaluated these in the  context of marine environment monitoring.

Changkyu Choi (center) with comitee and advisors. (Photo Harald Lykke Joakimsen)

Evaluation Committee

  • Associate Prof. Vedrana Dahl, Department of Applied Mathematics and Computer Science, Technical University of Denmark (1. Opponent)
  • Prof. Morten Goodwin, Department of Information and communication technology, University of Agder (2. Opponent)
  • Associate Prof. Benjamin Ricaud, Department of Physics and Technology, UiT (internal member and leader of the committee)

Supervisors

  • Professor Robert Jenssen, Department of Physics and Technology, UiT (main supervisor)
  • Associate Prof. Michael C. Kampffmeyer, Department of Physics and Technology, UiT (co-supervisor)
  • Senior Researcher Arnt-Børre Salberg, Norwegian Computing Centre (co-supervisor)

Summary of thesis

Marine environment monitoring has become increasingly significant due to the excessive exploitation of oceans, which detrimentally impacts ecosystems. Deep learning provides an effective monitoring approach by automating the analysis of vast amounts of observed image data, enabling stakeholders to make informed decisions regarding fishing quotas or conservation efforts. The success of deep learning is often attributed to its capacity to extract relevant features from data, without the need for handcrafted rules or heuristics. However, this capability is not without limitations, as the intricate feature extraction process of deep learning-based systems poses fundamental challenges. A lack of annotated data presents an inherent challenge for deep learning. The widespread success of deep learning has primarily relied on the ample availability of annotated data, while deep learning models encounter difficulties when learning from limited annotations. However, obtaining annotated data is expensive, particularly in the context of marine environment monitoring, as it is often a manual process demanding the expertise of domain specialists. Another challenge of deep learning is a lack of explainability. The black-box nature of deep learning models can make it difficult to understand how they arrive at their decisions. This hinders their adoption in critical decision-making processes, as stakeholders may be hesitant to trust models whose decision-making rationale is not transparent or interpretable. To address the challenges and further advance deep learning methodologies, this thesis proposes three novel deep learning methods, highlighting marine environment monitoring as an application domain.

Link to thesis in Munin

Acknowledgement

The PhD project was a collaboration between Institute of Marine Research, The Norwegian computing center and UiT The Arctic University of Norway. The project was funded by COGMAR and Visual Intelligence.

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