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arXiv · 2108.04907

Flow-based SVDD for anomaly detection

Marcin Sendera·Marek Śmieja·Łukasz Maziarka·Łukasz Struski·Przemysław Spurek·Jacek Tabor
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Abstract

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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Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor. 2021-08-10. Flow-based SVDD for anomaly detection. https://arxiv.org/abs/2108.04907

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