Autoencoder pre-training on unlabeled bridge monitoring data, followed by fine-tuning on a few hundred labels, raises anomaly detection F1 by 3 to 9 points over supervised training in two real bridge datasets.
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Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data
Autoencoder pre-training on unlabeled bridge monitoring data, followed by fine-tuning on a few hundred labels, raises anomaly detection F1 by 3 to 9 points over supervised training in two real bridge datasets.