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arXiv · 2512.21459

CCAD: Compressed Global Feature Conditioned Anomaly Detection

Xiao Jin·Liang Diao·Qixin Xiao·Yifan Hu·Ziqi Zhang·Yuchen Liu·Haisong Gu
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Abstract

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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Xiao Jin, Liang Diao, Qixin Xiao, Yifan Hu, Ziqi Zhang, Yuchen Liu, Haisong Gu. 2025-12-25. CCAD: Compressed Global Feature Conditioned Anomaly Detection. https://arxiv.org/abs/2512.21459

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