Outlier-Probability-Based Feature Adaptation for Robust Unsupervised Anomaly Detection on Contaminated Training Data

In the realm of large-scale industrial manufacturing, the precise detection of defective parts stands as a critical imperative. While current unsupervised anomaly detection algorithms exhibit commendable accuracy when applied to clean training datasets, their susceptibility to contaminated training...

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