CL-flow: strengthening the normalizing flows by contrastive learning for better anomaly detection

Shunfeng Wang; Yueyang Li; Haichi Luo

doi:10.1117/12.3033303

13 June 2024 CL-flow: strengthening the normalizing flows by contrastive learning for better anomaly detection

Shunfeng Wang, Yueyang Li, Haichi Luo

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318060 (2024) https://doi.org/10.1117/12.3033303
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Shunfeng Wang, Yueyang Li, and Haichi Luo "CL-flow: strengthening the normalizing flows by contrastive learning for better anomaly detection", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318060 (13 June 2024); https://doi.org/10.1117/12.3033303

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KEYWORDS

Education and training

Feature extraction

Performance modeling

Statistical modeling

Image restoration

Detection and tracking algorithms

Defect detection

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