Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift

• URL: http://arxiv.org/abs/2503.14910v1
• Date: Wed, 19 Mar 2025 05:25:52 GMT
• Title: Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
• Authors: Jingyi Liao, Xun Xu, Yongyi Su, Rong-Cheng Tu, Yifan Liu, Dacheng Tao, Xulei Yang,
• Abstract summary: Anomaly detection plays a crucial role in quality control for industrial applications.<n>Existing methods attempt to address domain shifts by training generalizable models.<n>Our proposed method demonstrates superior results compared with state-of-the-art anomaly detection and domain adaptation methods.
• Score: 51.24522135151649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor drift remains a significant challenge. Existing methods attempt to address domain shifts by training generalizable models but often rely on prior knowledge of target distributions and can hardly generalise to backbones designed for other data modalities. To overcome these limitations, we build upon memory-bank-based anomaly detection methods, optimizing a robust Sinkhorn distance on limited target training data to enhance generalization to unseen target domains. We evaluate the effectiveness on both 2D and 3D anomaly detection benchmarks with simulated distribution shifts. Our proposed method demonstrates superior results compared with state-of-the-art anomaly detection and domain adaptation methods.

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