Mai, Kimberly T;
Davies, Toby;
Griffin, Lewis D;
(2024)
Understanding the limitations of self-supervised learning for tabular anomaly detection.
Pattern Analysis and Applications
10.1007/s10044-023-01208-1.
(In press).
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Abstract
While self-supervised learning has improved anomaly detection in computer vision and natural language processing, it is unclear whether tabular data can benefit from it. This paper explores the limitations of self-supervision for tabular anomaly detection. We conduct several experiments spanning various pretext tasks on 26 benchmark datasets to understand why this is the case. Our results confirm representations derived from self-supervision do not improve tabular anomaly detection performance compared to using the raw representations of the data. We show this is due to neural networks introducing irrelevant features, which reduces the effectiveness of anomaly detectors. However, we demonstrate that using a subspace of the neural network’s representation can recover performance.
| Type: | Article |
|---|---|
| Title: | Understanding the limitations of self-supervised learning for tabular anomaly detection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10044-023-01208-1 |
| Publisher version: | http://dx.doi.org/10.1007/s10044-023-01208-1 |
| Language: | English |
| Additional information: | © 2024 Springer Nature. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189355 |
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