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DTSTART;TZID=Europe/Paris:20251113T140000
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DTSTAMP:20260501T234924
CREATED:20251022T060901Z
LAST-MODIFIED:20251028T122721Z
UID:7618-1763042400-1763046000@isdm.umontpellier.fr
SUMMARY:Deep Learning for Species Recognition under High Uncertainty: Application to jellyfish images
DESCRIPTION:Building 5\, 02.124\, Campus St Priest\nMachine Learning in Montpellier\, Theory & Practice – Matthieu de Castelbajac (Univ. Montpellier) \nCitizen science records are a valuable source of biodiversity data\, and even more essential to help track mobile marine species like jellyfish. However\, these records can be highly uncertain\, containing many potential errors and biases. They are typically validated by experts\, which is impractical at scale. Although deep learning methods for automatic validation have shown promising results\, they fail to account for the uncertainty present in both the input data and their predictions. Here\, we present a semi-automated method to support record validation at scale while providing strong statistical guarantees\, including for highly uncertain citizen science records.
URL:https://isdm.umontpellier.fr/event/deep-learning-for-species-recognition-under-high-uncertainty-application-to-jellyfish-images/
CATEGORIES:Séminaire
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