Unsupervised Outlier Detection using Memory and Contrastive Learning

• URL: http://arxiv.org/abs/2107.12642v1
• Date: Tue, 27 Jul 2021 07:35:42 GMT
• Title: Unsupervised Outlier Detection using Memory and Contrastive Learning
• Authors: Ning Huyan, Dou Quan, Xiangrong Zhang, Xuefeng Liang, Jocelyn Chanussot, Licheng Jiao
• Abstract summary: We think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers. We propose a framework, MCOD, using a memory module and a contrastive learning module. Our proposed MCOD achieves a considerable performance and outperforms nine state-of-the-art methods.
• Score: 53.77693158251706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Outlier detection is one of the most important processes taken to create good, reliable data in machine learning. The most methods of outlier detection leverage an auxiliary reconstruction task by assuming that outliers are more difficult to be recovered than normal samples (inliers). However, it is not always true, especially for auto-encoder (AE) based models. They may recover certain outliers even outliers are not in the training data, because they do not constrain the feature learning. Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers. We then propose a framework, MCOD, using a memory module and a contrastive learning module. The memory module constrains the consistency of features, which represent the normal data. The contrastive learning module learns more discriminating features, which boosts the distinction between outliers and inliers. Extensive experiments on four benchmark datasets show that our proposed MCOD achieves a considerable performance and outperforms nine state-of-the-art methods.

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