UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection
Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which ty...
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MDPI AG
2024-12-01
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| author | Jianmei Zhong Yanzhi Song |
| author_facet | Jianmei Zhong Yanzhi Song |
| author_sort | Jianmei Zhong |
| collection | DOAJ |
| description | Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. Research specifically targeting multi-class anomaly detection remains relatively limited. In this work, we propose a powerful unified normalizing flow for multi-class anomaly detection, which we call UniFlow. A multi-cognitive visual adapter (Mona) is employed in our method as the feature adaptation layer to adapt image features for both the multi-class anomaly detection task and the normalizing flow model, facilitating the learning of general knowledge of normal images across multiple categories. We adopt multi-cognitive convolutional networks with high capacity to construct the coupling layers within the normalizing flow model for more effective multi-class distribution modeling. In addition, we employ a multi-scale feature fusion module to aggregate features from various levels, thereby obtaining fused features with enhanced expressive capabilities. UniFlow achieves a class-average image-level AUROC of 99.1% and a class-average pixel-level AUROC of 98.0% on MVTec AD, outperforming the SOTA multi-class anomaly detection methods. Extensive experiments on three benchmark datasets, MVTec AD, VisA, and BTAD, demonstrate the efficacy and superiority of our unified normalizing flow in multi-class anomaly detection. |
| format | Article |
| id | doaj-art-8decdfe9357f4f7aae6c7fc508a6846c |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-8decdfe9357f4f7aae6c7fc508a6846c2025-08-20T02:55:38ZengMDPI AGInformation2078-24892024-12-01151279110.3390/info15120791UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly DetectionJianmei Zhong0Yanzhi Song1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, ChinaSchool of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, ChinaMulti-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. Research specifically targeting multi-class anomaly detection remains relatively limited. In this work, we propose a powerful unified normalizing flow for multi-class anomaly detection, which we call UniFlow. A multi-cognitive visual adapter (Mona) is employed in our method as the feature adaptation layer to adapt image features for both the multi-class anomaly detection task and the normalizing flow model, facilitating the learning of general knowledge of normal images across multiple categories. We adopt multi-cognitive convolutional networks with high capacity to construct the coupling layers within the normalizing flow model for more effective multi-class distribution modeling. In addition, we employ a multi-scale feature fusion module to aggregate features from various levels, thereby obtaining fused features with enhanced expressive capabilities. UniFlow achieves a class-average image-level AUROC of 99.1% and a class-average pixel-level AUROC of 98.0% on MVTec AD, outperforming the SOTA multi-class anomaly detection methods. Extensive experiments on three benchmark datasets, MVTec AD, VisA, and BTAD, demonstrate the efficacy and superiority of our unified normalizing flow in multi-class anomaly detection.https://www.mdpi.com/2078-2489/15/12/791multi-class anomaly detectionanomaly localizationdefect detectionnormalizing flow |
| spellingShingle | Jianmei Zhong Yanzhi Song UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection Information multi-class anomaly detection anomaly localization defect detection normalizing flow |
| title | UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection |
| title_full | UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection |
| title_fullStr | UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection |
| title_full_unstemmed | UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection |
| title_short | UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection |
| title_sort | uniflow unified normalizing flow for unsupervised multi class anomaly detection |
| topic | multi-class anomaly detection anomaly localization defect detection normalizing flow |
| url | https://www.mdpi.com/2078-2489/15/12/791 |
| work_keys_str_mv | AT jianmeizhong uniflowunifiednormalizingflowforunsupervisedmulticlassanomalydetection AT yanzhisong uniflowunifiednormalizingflowforunsupervisedmulticlassanomalydetection |