Masked Contrastive Learning for Anomaly Detection
Masked Contrastive Learning for Anomaly Detection
Hyunsoo Cho, Jinseok Seol, Sang-goo Lee
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 1434-1441.
https://doi.org/10.24963/ijcai.2021/198
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 shown promising results. 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 showing pronounced results 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.
Keywords:
Data Mining: Anomaly/Outlier Detection
Machine Learning: Clustering
Data Mining: Clustering