MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data

Jiaxin Yin, Yuanyuan Qiao, Zitang Zhou, Xiangchao Wang, Jie Yang

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Keywords: anomaly detection, tabular data, self-supervised learning

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Abstract: This paper addresses the problem of anomaly detection in tabular data, which is usually implemented in an one-class classification setting where the training set only contains normal samples. Inspired by the success of masked image/language modeling in vision and natural language domains, we extend masked modeling methods to address this problem by capturing intrinsic correlations between features in training set. Thus, a sample deviate from such correlations is related to a high possibility of anomaly. To obtain multiple and diverse correlations, we propose a novel masking strategy which generates multiple masks by learning, and design a diversity loss to reduce the similarity of different masks. Extensive experiments show our method achieves state-of-the-art performance. We also discuss the interpretability from the perspective of each individual feature and correlations between features.

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Primary Area: representation learning for computer vision, audio, language, and other modalities

Submission Number: 7570