Unsupervised Outlier Detection Using Memory and Contrastive Learning
Outlier detection is to separate anomalous data from inliers in the dataset. Recently, the most deep learning methods of outlier detection leverage an auxiliary reconstruction task by assuming that outliers are more difficult to recover than normal samples (inliers). However, it is not always true i...
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Détails bibliographiques
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 31(2022) vom: 05., Seite 6440-6454
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| Auteur principal: |
Huyan, Ning
(Auteur) |
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| Autres auteurs: |
Quan, Dou,
Zhang, Xiangrong,
Liang, Xuefeng,
Chanussot, Jocelyn,
Jiao, Licheng |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2022
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Sujets: | Journal Article |
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