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Recently, reconstruction\u2010based anomaly detection methods have made great progress. However, most of the existing methods take reconstructing the original image as the goal of latent feature learning. Due to lack of effective semantic guidance, latent features have intrinsic characteristics which retain redundant details of spatial structure. Such information is too general and cause over\u2010expression problem. To solve this problem, in this paper, dual transformation\u2010aware embeddings are coined which aims to achieve a stable model to learn high\u2010level latent features in a self\u2010supervised manner. To be more specific, the authors try to extract transformation\u2010detectable feature embeddings for both structure and content views which explore the regular pattern under different transformations in normal situations. In addition, the relationship between the original feature and the transformed feature is established. Based on such relationship, the latent feature of generated image to predict transformation parameter is extracted. Then, a transformation\u2010consistency regularization is proposed to constrain decoder to generate high\u2010quality image with high\u2010level consistency and achieve a more stable model. Experiments on MVTec\u2010AD and CIFAR10 datasets prove the effectiveness and robustness of the proposed\u00a0method.<\/jats:p>","DOI":"10.1049\/ipr2.12438","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T12:04:43Z","timestamp":1644321883000},"page":"1657-1668","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised anomaly detection via dual transformation\u2010aware embeddings"],"prefix":"10.1049","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1145-234X","authenticated-orcid":false,"given":"Zhipeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering Tianjin University  Tianjin China"}]},{"given":"Chunping","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering Tianjin University  Tianjin China"}]},{"given":"Bangbang","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering Tianjin University  Tianjin China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering Tianjin University  Tianjin China"}]},{"given":"Zhicheng","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Engineering Tibet University  Lhasa China"}]},{"given":"Zhiqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Engineering Tibet University  Lhasa China"},{"name":"Department of Electrical Engineering Wright State University  Dayton Ohio USA"}]}],"member":"265","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"Zhou K. 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