Master of Science Iver Martinsen will Wednesday August 20th, 2025, at 13:15 hold his Thesis Defense for the PhD degree in Science. The title of the thesis is:
« Uncertainty and Representation Learning in Image Recognition: Advancing Deep Learning for Microfossil Analysis »
Alongside the rapid development of deep learning, image recognition has achieved significant advances, with particularly promise within the supervised learning paradigm. While current models demonstrate strong predictive performance on benchmark datasets, deep neural networks often lack robustness, exhibiting unpredictable failures in operation. Challenges such as out-of-distribution data from unseen groups and domain-shift between training and test data highlight the need for accurate uncertainty estimates. Although many methods have been proposed, current approaches fail to address all sources of uncertainty comprehensively. Additionally, existing uncertainty measures are insufficient for practical evaluation, as they do not adequately assess the real-world usability. Microfossil analysis is one of many applications that stands to benefit from deep learning advancements. Microfossils are abundant worldwide and serve as indicators of past environments and subsurface structures, making them invaluable for both academic research and the energy sector. Recent digitization efforts have also produced large volumes of unlabeled data, underscoring the importance of methodologies that can exploit this. This thesis aims to address three core challenges in deep learning methodology: uncertainty, self-supervised learning, and interpretability. First, this work presents advances in uncertainty estimation and microfossil analysis. From a methodological perspective, uncertainty is advanced in two directions: by comparing deep learning uncertainty to human participants, leading to the proposal of new uncertainty measures, and by introducing a novel method for estimating uncertainty in out-of-distribution scenarios. Second, this thesis establishes a new state-of-the-art in microfossil analysis by using self-supervised learning. These advancements enable the development of an automated pipeline for microfossil analysis and the automatic generation of biostratigraphy reports. Finally, this work investigates interpretability methods, with a particular focus on analyzing a state-of-the-art model for sea ice prediction. By addressing these challenges, this thesis contributes to advancing deep learning methodologies while demonstrating their potential for impactful applications in microfossil analysis.
Supervisory Committee:
1st Opponent: Professor Anders Bjorholm Dahl, Denmark Technical University
2nd Opponent: Professor Arnoldo Frigessi, UiO
Internal member and leader of the committee: Associate professor Elisabeth Wetzer, IFT, UiT
The defence and trial lecture will be streamed from these following links at Panopto:
Defence (13:15 - 16:00)
Trial Lecture (10:15 - 11:15)
The thesis is available at Munin Here.