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Brittle Features May Help Anomaly Detection

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Abstract

One-class anomaly detection is challenging. A representation that clearly distinguishes anomalies from normal data is ideal, but arriving at this representation is difficult since only normal data is available at training time. We examine the performance of representations, transferred from auxiliary tasks, for anomaly detection. Our re...More

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Author
Kimberly T. Mai
Kimberly T. Mai
Toby Davies
Toby Davies
Lewis D. Griffin
Lewis D. Griffin
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