CCAD: Compressed Global Feature Conditioned Anomaly Detection

• URL: http://arxiv.org/abs/2512.21459v1
• Date: Thu, 25 Dec 2025 01:33:00 GMT
• Title: CCAD: Compressed Global Feature Conditioned Anomaly Detection
• Authors: Xiao Jin, Liang Diao, Qixin Xiao, Yifan Hu, Ziqi Zhang, Yuchen Liu, Haisong Gu,
• Abstract summary: We propose a novel method named Compressed Global Feature Conditioned Anomaly Detection (CCAD)<n>CCAD synergizes the strengths of both paradigms by adapting global features as a new modality condition for the reconstruction model.<n>Extensive experiments demonstrate that CCAD consistently outperforms state-of-the-art methods in terms of AUC.
• Score: 17.713315314682458
• License: http://creativecommons.org/licenses/by/4.0/
• 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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