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Group Anomaly Detection Using Deep Generative Models

  • Raghavendra Chalapathy
  • Edward TothEmail author
  • Sanjay Chawla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)

Abstract

Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. Code related to this paper is available at: https://github.com/raghavchalapathy/gad, https://www.cs.cmu.edu/~lxiong/gad/gad.html, https://github.com/jorjasso/SMDD-group-anomaly-detection, https://github.com/cjlin1/libsvm.

Keywords

Group anomaly detection Adversarial Variational Auto-encoders 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Raghavendra Chalapathy
    • 1
  • Edward Toth
    • 2
    Email author
  • Sanjay Chawla
    • 3
  1. 1.The University of Sydney and Capital Markets CRCSydneyAustralia
  2. 2.School of Information TechnologiesThe University of SydneySydneyAustralia
  3. 3.Qatar Computing Research Institute, HBKUDohaQatar

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