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Active anomaly detection based on deep one-class classification
In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples.
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Therefore, it has been a common practice to learn normality under the assumption that anomalous data are absent in a training dataset, which we call normality assumption.
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available.
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