CCAD：压缩全局特征条件异常检测
CCAD: Compressed Global Feature Conditioned Anomaly Detection
AI总结：
CCAD通过结合全局特征条件和自适应压缩机制，提升异常检测的鲁棒性和效率，实验显示其在AUC指标上优于现有方法。
摘要：
异常检测在工业中具有重要价值，尤其是在异常数据有限的场景中。目前，基于重建和无监督表征的方法是主要研究方向。然而，无监督表征方法在域移时难以提取稳健特征，而基于重建的方法则因约束不足导致训练效率低和性能下降。为解决这些问题，我们提出了一种名为压缩全局特征条件异常检测（CCAD）的新方法。CCAD通过将全局特征作为重建模型的新模态条件，结合两种范式的优势。此外，我们设计了一种自适应压缩机制，以增强泛化能力和训练效率。大量实验表明，CCAD在AUC方面始终优于现有最先进方法，同时实现更快的收敛速度。此外，我们还贡献了一个重新整理和重新注释的DAGM 2007数据集的新注释，以进一步验证我们方法的有效性。复现主要结果的代码可在https://github.com/chloeqxq/CCAD上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
