Using conformal prediction for novelty detection in microfossil analysis: Iver Martinsen (UiT)
Автор: SFI Visual Intelligence
Загружено: 2025-03-28
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Iver Martinsen, a PhD Candidate at the UiT Machine Learning Group, gave a presentation titled "Using conformal prediction for novelty detection in microfossil analysis" on March 27th 2025 as part of the Visual Intelligence Online Seminar series.
Abstract:
Microfossil analysis is crucial for subsurface mapping, such as matching strata between wells. Traditionally, specialist geoscientists manually examine numerous samples to identify key microfossil species. The Norwegian Offshore Directorate's digitalization of microfossil samples, combined with AI advancements, offers new possibilities for automating analysis. Unsupervised representation learning, a key AI research area, can generate useful image representations from large datasets without labels. While previous work has shown promising results for classifying limited classes, challenges remain in realistic settings with unknown species. Our recent work integrates unsupervised representation learning and uncertainty estimation to automate fossil analysis. We detail our approach and findings in three parts: training AI models using self-supervised learning methods, developing a conformal prediction method to manage diverse data, and creating distribution charts of fossils across multiple wells.
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