针对工业异常检测中的分布偏移问题，提出鲁棒的分布对齐方法。
Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
- 基于记忆库方法，用有限目标数据优化鲁棒Sinkhorn距离。
- 在2D和3D基准上，对模拟分布偏移有更强的泛化能力。
- 适用于不同模态模型，无需先验分布知识，适合工业质检场景。
异常检测在工业质量控制中至关重要。然而，在光照变化或传感器漂移等未见领域偏移下保持鲁棒性仍是重大挑战。现有方法通过训练泛化模型应对领域偏移，但常依赖目标分布的先验知识，且难以适配其他数据模态的主干网络。为此，我们基于记忆库型异常检测方法，在有限目标训练数据上优化鲁棒的Sinkhorn距离，以提升对未见目标领域的泛化能力。我们在包含模拟分布偏移的2D与3D异常检测基准上评估了该方法的有效性。实验结果表明，相比当前最优的异常检测与领域自适应方法，本方法表现更优。
原文摘要 · Abstract (English)
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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