TY - CONF
T1 - ANOMALY DETECTION FOR TABULAR DATA WITH INTERNAL CONTRASTIVE LEARNING
AU - Shenkar, Tom
AU - Wolf, Lior
N1 - Publisher Copyright:
© 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85140845340&partnerID=8YFLogxK
M3 - ???researchoutput.researchoutputtypes.contributiontoconference.paper???
AN - SCOPUS:85140845340
T2 - 10th International Conference on Learning Representations, ICLR 2022
Y2 - 25 April 2022 through 29 April 2022
ER -