A data-curation pipeline combining AIS ship data with hierarchical k-means clustering improves self-supervised learning for underwater acoustic ship classification.
The study demonstrates that curation is a key aspect in extracting accurate SSL model representations from unlabeled un- derwater recordings
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Automated data curation for self-supervised learning in underwater acoustic analysis
A data-curation pipeline combining AIS ship data with hierarchical k-means clustering improves self-supervised learning for underwater acoustic ship classification.