{"ID":2825108,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21459","arxiv_id":"2512.21459","title":"CCAD: Compressed Global Feature Conditioned Anomaly Detection","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.","short_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 s...","url_abs":"https://arxiv.org/abs/2512.21459","url_pdf":"https://arxiv.org/pdf/2512.21459v1","authors":"[\"Xiao Jin\",\"Liang Diao\",\"Qixin Xiao\",\"Yifan Hu\",\"Ziqi Zhang\",\"Yuchen Liu\",\"Haisong Gu\"]","published":"2025-12-25T01:33:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":605636,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2825108,"paper_url":"https://arxiv.org/abs/2512.21459","paper_title":"CCAD: Compressed Global Feature Conditioned Anomaly Detection","repo_url":"https://github.com/chloeqxq/CCAD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}