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Masked Contrastive Learning for Anomaly Detection
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Web of Science
Cited 8 time in Scopus
- Authors
- Issue Date
- 2021-08
- Citation
- IJCAI International Joint Conference on Artificial Intelligence, pp.1434-1441
- Abstract
- © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.Detecting anomalies is one fundamental aspect of a safety-critical software system, however, it remains a long-standing problem. Numerous branches of works have been proposed to alleviate the complication and have demonstrated their efficiencies. In particular, self-supervised learning based methods are spurring interest due to their capability of learning diverse representations without additional labels. Among self-supervised learning tactics, contrastive learning is one specific framework validating their superiority in various fields, including anomaly detection. However, the primary objective of contrastive learning is to learn task-agnostic features without any labels, which is not entirely suited to discern anomalies. In this paper, we propose a task-specific variant of contrastive learning named masked contrastive learning, which is more befitted for anomaly detection. Moreover, we propose a new inference method dubbed self-ensemble inference that further boosts performance by leveraging the ability learned through auxiliary self-supervision tasks. By combining our models, we can outperform previous state-of-the-art methods by a significant margin on various benchmark datasets.
- ISSN
- 1045-0823
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