Boosting semi-supervised anomaly detection via contrasting synthetic images

Sheng Feng Yu, Wei Chen Chiu

研究成果: Conference contribution同行評審

摘要

In this paper we propose to tackle the problem of semi-supervised anomaly detection, which aims to learn the outlier detector from the training set composed of only inliers. Built upon the recent advances of introducing contrastive learning to achieve the state-of-the-art of anomaly detection, we propose a simple but effective extension to further boost the performance via integrating the contrastive learning and the generative model of inliers into a unified framework. On one hand, the contrastive learning amongst the real samples and synthetic ones produced by the generative model improves the representation learning; on the other hand, the generative model learning is also benefited from the contrastive learning. We conduct extensive experiments to demonstrate the efficacy of our proposed method to advance anomaly detection, its superiority against several baselines, and the contribution of our model designs.

原文English
主出版物標題Proceedings of MVA 2021 - 17th International Conference on Machine Vision Applications
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9784901122207
DOIs
出版狀態Published - 25 7月 2021
事件17th International Conference on Machine Vision Applications, MVA 2021 - Aichi, Japan
持續時間: 25 7月 202127 7月 2021

出版系列

名字Proceedings of MVA 2021 - 17th International Conference on Machine Vision Applications

Conference

Conference17th International Conference on Machine Vision Applications, MVA 2021
國家/地區Japan
城市Aichi
期間25/07/2127/07/21

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