Improved autoencoder for unsupervised anomaly detection
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DOI: 10.1002/int.22582
Abstract: Deep autoencoder‐based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish… read more here.
Keywords: detection; anomaly detection; improved autoencoder; unsupervised anomaly ... See more keywords