MAEDAY: MAE for few- and zero-shot AnomalY-Detection

Author:

Schwartz EliORCID,Arbelle AssafORCID,Karlinsky Leonid,Harary Sivan,Scheidegger Florian,Doveh Sivan,Giryes Raja

Funder

European Research Council

Publisher

Elsevier BV

Reference36 articles.

1. The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection;Bergmann;Int. J. Comput. Vis.,2021

2. Segment any anomaly without training via hybrid prompt regularization;Cao,2023

3. Anomaly detection through registration;Chen;Pattern Recognit.,1999

4. Sub-image anomaly detection with deep pyramid correspondences;Cohen,2020

5. Padim: a patch distribution modeling framework for anomaly detection and localization;Defard,2021

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