用压缩全局特征提升异常检测精度与训练速度
CCAD: Compressed Global Feature Conditioned Anomaly Detection
- 将全局特征作为重建模型的新条件输入
- 在DAGM 2007上达到更高AUC并更快收敛
- 适合工业场景下小样本异常检测
异常检测在工业应用中具有重要意义，尤其在异常数据有限的场景下。当前主流方法为基于重构和无监督表示的方法。然而，无监督表示方法在领域迁移下难以提取鲁棒特征，而基于重构的方法常因约束不足导致训练效率低、性能下降。为此，我们提出一种新方法——压缩全局特征条件异常检测（CCAD），通过将全局特征作为新模态条件融入重建模型，结合自适应压缩机制，同时提升泛化能力与训练效率。大量实验表明，CCAD在AUC指标上持续优于现有最优方法，并实现更快收敛。此外，我们还对DAGM 2007数据集进行了重新整理与标注，以进一步验证方法有效性。代码已公开于https://github.com/chloeqxq/CCAD。
原文摘要 · Abstract (English)
Anomaly detection holds considerable industrial significance, especially in scenarios with limited anomalous data. Currently, reconstruction-based and unsupervised representation-based approaches are the primary focus. However, unsupervised representation-based methods struggle to extract robust features under domain shift, whereas reconstruction-based methods often suffer from low training efficiency and performance degradation due to insufficient constraints. To address these challenges, we propose a novel method named Compressed Global Feature Conditioned Anomaly Detection (CCAD). CCAD synergizes the strengths of both paradigms by adapting global features as a new modality condition for the reconstruction model. Furthermore, we design an adaptive compression mechanism to enhance both generalization and training efficiency. Extensive experiments demonstrate that CCAD consistently outperforms state-of-the-art methods in terms of AUC while achieving faster convergence. In addition, we contribute a reorganized and re-annotated version of the DAGM 2007 dataset with new annotations to further validate our method's effectiveness. The code for reproducing main results is available at https://github.com/chloeqxq/CCAD.
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