Understanding the limitations of self-supervised learning for tabular anomaly detection

arXiv:2309.08374v3 Announce Type: replace
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 …

abstract and natural language processing anomaly anomaly detection arxiv benefit computer computer vision cs.lg data detection language language processing limitations natural natural language natural language processing paper processing self-supervised learning supervised learning supervision tabular tabular data tasks type understanding vision