{"type":"ScholarlyArticle","title":"Masked Contrastive Learning for Anomaly Detection","authors":[{"name":"Hyunsoo Cho","slug":"hyunsoo-cho","given":"Hyunsoo","family":"Cho","affiliation":""},{"name":"Jinseok Seol","slug":"jinseok-seol","given":"Jinseok","family":"Seol","affiliation":""},{"name":"Sang-goo Lee","slug":"sang-goo-lee","given":"Sang-goo","family":"Lee","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/198","url":"https://ijcai.org/proceedings/2021/198","pdf":"https://ijcai.org/proceedings/2021/0198.pdf","markdown":"https://ijcai.org/proceedings/2021/198.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/198","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":198,"pages":{"first":1434,"last":1441},"keywords":["Data Mining: Clustering","Machine Learning: Clustering","Data Mining: Anomaly/Outlier Detection"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}