Transferable visual pattern memory network for domain adaptation in anomaly detection. (May 2023)

• Record Type: Journal Article
• Authors: Fan, Cangning; Jin, Ye; Liu, Peng; Zhao, Wei
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Anomaly detection transfer -- Anomaly detection -- Domain adaptation -- Memory network -- Transfer learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.106013
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Anomaly detection transfer aims to utilize knowledge learned from source anomaly detection task to improve the performance of target anomaly detection task. Conventional methods typically assume that labeled normal or abnormal data are available in the source or target domain. However, many real-world applications do not satisfy this assumption because such labels are hard to collect. This study focuses on the case where anomalous labels are unavailable. More specifically, a rarely studied scenario in which the target domain contains unlabeled normal and abnormal instances, whereas only normal instances are available in the source domain, is addressed. To this end, a transferable visual pattern memory network was designed to transfer knowledge for anomaly detection tasks. The network comprises an adversarial domain adaptation method to extract transferable visual patterns, and a memory module utilized to store these patterns. The model utilizes transferable patterns stored in memory to identify anomalous samples. Moreover, a self-supervised objective is integrated to enhance the discriminability of target abnormal instances, thereby improving the anomaly detection performance. The results of extensive experiments conducted on publicly available anomaly-detection datasets verified the efficacy of the proposed approach. Graphical abstract: Highlights: This study focuses on a rarely studied anomaly detection transfer scenario with little supervised information. An adversarial domain adaptation method is proposed to extract transferable visual patterns to transfer knowledge. A self-supervised objective is integrated to enhance the discriminability of anomalies.