Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

Albert Akhriev; Jakub Marecek

Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

Computer Vision and Pattern Recognition 2019-12-11 v1 Machine Learning Image and Video Processing

Authors: Albert Akhriev , Jakub Marecek

Abstract

Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model. We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used to detect anomalies at the same time. In the detection of anomalies, we utilise a novel thresholding mechanism, based on value at risk (VaR). We compare the resulting convolutional neural network (CNN) against a number of subspace methods, and present results on changedetection net.

Keywords

variational autoencoder anomaly detection deep neural network

Cite

@article{arxiv.1912.04418,
  title  = {Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection},
  author = {Albert Akhriev and Jakub Marecek},
  journal= {arXiv preprint arXiv:1912.04418},
  year   = {2019}
}

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