Flow-based SVDD for anomaly detection [article]

Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor
<span title="2021-08-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersphere into a single point. Experiments show that FlowSVDD achieves comparable results to the current state-of-the-art methods and significantly outperforms related deep SVDD methods on benchmark datasets.
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