Flow-based SVDD for anomaly detection

• URL: http://arxiv.org/abs/2108.04907v1
• Date: Tue, 10 Aug 2021 20:33:15 GMT
• Title: Flow-based SVDD for anomaly detection
• Authors: Marcin Sendera, Marek \'Smieja, {\L}ukasz Maziarka, {\L}ukasz Struski, Przemys{\l}aw Spurek, Jacek Tabor
• Abstract summary: FlowSVDD is a flow-based one-class classifier for anomaly/outliers detection. 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.
• Score: 12.319113026372966
• License: http://creativecommons.org/licenses/by/4.0/
• 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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