Are generative deep models for novelty detection truly better?

Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets. We provide statistical comparison of the selected models, in many con...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Škvára, Vít, Pevný, Tomáš, Šmídl, Václav
Format: Article
Language:English
Subjects:
Anomalies
Image detection
Statistical methods
Online Access:Get full text
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Summary:Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets. We provide statistical comparison of the selected models, in many configurations, architectures and hyperparamaters. We arrive to conclusion that performance of the generative models is determined by the process of selection of their hyperparameters. Specifically, performance of the deep generative models deteriorates with decreasing amount of anomalous samples used in hyperparameter selection. In practical scenarios of anomaly detection, none of the deep generative models systematically outperforms the kNN.
ISSN:2331-8422