ANOMALY DETECTION FOR TABULAR DATA WITH INTERNAL CONTRASTIVE LEARNING

Tom Shenkar, Lior Wolf

Research output: Contribution to conferencePaperpeer-review

18 Scopus citations

Abstract

We consider the task of finding out-of-class samples in tabular data, where little can be assumed on the structure of the data. In order to capture the structure of the samples of the single training class, we learn mappings that maximize the mutual information between each sample and the part that is masked out. The mappings are learned by employing a contrastive loss, which considers only one sample at a time. Once learned, we can score a test sample by measuring whether the learned mappings lead to a small contrastive loss using the masked parts of this sample. Our experiments show that our method leads by a sizable accuracy gap in comparison to the literature and that the same default rule of hyperparameters selection provides state-of-the-art results across benchmarks.

Original languageEnglish
StatePublished - 2022
Event10th International Conference on Learning Representations, ICLR 2022 - Virtual, Online
Duration: 25 Apr 202229 Apr 2022

Conference

Conference10th International Conference on Learning Representations, ICLR 2022
CityVirtual, Online
Period25/04/2229/04/22

Funding

FundersFunder number
Horizon 2020 Framework Programme
European Research Council
Horizon 2020ERC CoG 725974

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