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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\u2019s representation can recover performance.<\/jats:p>","DOI":"10.1007\/s10044-023-01208-1","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:08:30Z","timestamp":1710270510000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Understanding the limitations of self-supervised learning for tabular anomaly detection"],"prefix":"10.1007","volume":"27","author":[{"given":"Kimberly T.","family":"Mai","sequence":"first","affiliation":[]},{"given":"Toby","family":"Davies","sequence":"additional","affiliation":[]},{"given":"Lewis D.","family":"Griffin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"1208_CR1","volume-title":"Using self-supervised learning can improve model robustness and uncertainty","author":"D Hendrycks","year":"2019","unstructured":"Hendrycks D, Mazeika M, Kadavath S, Song D (2019) Using self-supervised learning can improve model robustness and uncertainty. 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