{"ID":2829715,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11284","arxiv_id":"2512.11284","title":"RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection","abstract":"Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.","short_abstract":"Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale....","url_abs":"https://arxiv.org/abs/2512.11284","url_pdf":"https://arxiv.org/pdf/2512.11284v1","authors":"[\"Rongcheng Wu\",\"Hao Zhu\",\"Shiying Zhang\",\"Mingzhe Wang\",\"Zhidong Li\",\"Hui Li\",\"Jianlong Zhou\",\"Jiangtao Cui\",\"Fang Chen\",\"Pingyang Sun\",\"Qiyu Liao\",\"Ye Lin\"]","published":"2025-12-12T05:07:09Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}