{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T06:06:14Z","timestamp":1769925974964,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Unsupervised anomaly detection is to detect previously unseen rare samples without any prior knowledge about them. With the emergence of deep learning, many methods employ normal data reconstruction to train detection models, which is expected to yield relatively large errors when reconstructing anomalies. However, recent studies find that anomalies can be overgeneralized, resulting in reconstruction errors as small as normal samples. In this paper, we examine the anomaly overgeneralization problem and propose global semantic information learning. Normal and anomalous samples may share the same local feature such as textures, edges, and corners, but have separability at the global semantic level. To address this, we propose a novel cascade memory architecture designed to capture global semantic information in the latent space and introduce a configurable sparsification and random forgetting mechanism. Our proposed method achieves state-of-the-art experimental results on different public benchmarks, without the introduction of any additional auxiliary loss terms. The code is available at https:\/\/github.com\/LiJiahao-Alex\/Cascade-Memory.<\/jats:p>","DOI":"10.3233\/faia240822","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:28:36Z","timestamp":1729171716000},"source":"Crossref","is-referenced-by-count":2,"title":["Cascade Memory for Unsupervised Anomaly Detection"],"prefix":"10.3233","author":[{"given":"Jiahao","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"},{"name":"Pengcheng Laboratory"},{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device"},{"name":"University of Chinese Academy of Sciences"},{"name":"AIER Eye Hospital Group Co., Ltd."}]},{"given":"Yiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"},{"name":"Pengcheng Laboratory"},{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Yunbing","family":"Xing","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"},{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device"},{"name":"University of Chinese Academy of Sciences"},{"name":"AIER Eye Hospital Group Co., Ltd."}]},{"given":"Yang","family":"Gu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"},{"name":"Pengcheng Laboratory"},{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device"},{"name":"University of Chinese Academy of Sciences"}]},{"given":"Xiangyuan","family":"Lan","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:28:36Z","timestamp":1729171716000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240822","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}