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NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Dong, Hao, Frusque, Gaëtan, Zhao, Yue, Chatzi, Eleni, Fink, Olga
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
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