Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection

Authors

  • Vercruyssen Vincent KU Leuven
  • Meert Wannes KU Leuven
  • Davis Jesse KU Leuven

DOI:

https://doi.org/10.1609/aaai.v34i04.6068

Abstract

Anomaly detection attempts to identify instances that deviate from expected behavior. Constructing performant anomaly detectors on real-world problems often requires some labeled data, which can be difficult and costly to obtain. However, often one considers multiple, related anomaly detection tasks. Therefore, it may be possible to transfer labeled instances from a related anomaly detection task to the problem at hand. This paper proposes a novel transfer learning algorithm for anomaly detection that selects and transfers relevant labeled instances from a source anomaly detection task to a target one. Then, it classifies target instances using a novel semi-supervised nearest-neighbors technique that considers both unlabeled target and transferred, labeled source instances. The algorithm outperforms a multitude of state-of-the-art transfer learning methods and unsupervised anomaly detection methods on a large benchmark. Furthermore, it outperforms its rivals on a real-world task of detecting anomalous water usage in retail stores.

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Published

2020-04-03

How to Cite

Vincent, V., Wannes, M., & Jesse, D. (2020). Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6054-6061. https://doi.org/10.1609/aaai.v34i04.6068

Issue

Section

AAAI Technical Track: Machine Learning