Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly-Supervised Anomaly Detection

Zhi Cao1,2; Ye Shi3; Yu-Cheng Chang2; Xin Yao4; Chin-Teng Lin2

2024

10.1109/TFUZZ.2024.3412435

IEEE Transactions on Fuzzy Systems   影响因子和分区

1941-0034

Weakly-supervised anomaly detection (WSAD) has gained increasing attention due to its core idea of enhancing the performance of unsupervised anomaly detection by leveraging prior knowledge from a limited number of labeled anomalies. In this paper, we introduce a novel WSAD framework that surpasses current state-of-the-art methods in terms of accuracy, exhibits greater robustness to data uncertainty, and is more efficient in utilizing limited labeled anomalies. Our method is built upon twin fuzzy networks (TFN) that learn robust fuzzy if-then rules from a pairwise training set. TFN can extract informative prototypes of training instances, exploiting the very few labeled anomalies efficiently. A two-stage sequential training scheme, comprising fuzzy C-means clustering and interpolation consistency regularization, ensures that the fuzzy rules form a solid foundation for anomaly detection while improving TFN's generalization ability. The training process of TFN relies on closed-form optimization rather than gradient-based methods, leading to significantly faster training speeds. Comprehensive experiments conducted on numerous real-world datasets confirm the advantages of the TFN framework over existing alternatives.

EI

第一

ENGINEERING

1.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.CIBCI lab, Australian AI Institute, the School of Computer Science, University of Technology, Sydney, NSW, Australia
3.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
4.School of Data Science, Lingnan University, Hong Kong SAR, China

Zhi Cao,Ye Shi,Yu-Cheng Chang,et al. Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly-Supervised Anomaly Detection[J]. IEEE Transactions on Fuzzy Systems,2024,PP(99).

Zhi Cao,Ye Shi,Yu-Cheng Chang,Xin Yao,&Chin-Teng Lin.(2024).Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly-Supervised Anomaly Detection.IEEE Transactions on Fuzzy Systems,PP(99).

Zhi Cao,et al."Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly-Supervised Anomaly Detection".IEEE Transactions on Fuzzy Systems PP.99(2024).