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