{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:46:29Z","timestamp":1773801989968,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1609\/aaai.v40i13.38048","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:07:46Z","timestamp":1773792466000},"page":"10736-10744","source":"Crossref","is-referenced-by-count":0,"title":["RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection"],"prefix":"10.1609","volume":"40","author":[{"given":"Rongcheng","family":"Wu","sequence":"first","affiliation":[]},{"given":"Hao","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shiying","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mingzhe","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhidong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jianlong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Jiangtao","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Pingyang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Qiyu","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Lin","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38048\/42010","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38048\/42010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:07:46Z","timestamp":1773792466000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i13.38048","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}