Eli Schwartz, Leonid Karlinsky, et al.
NeurIPS 2018
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly Detection (FSAD). We also show the same method works surprisingly well for the novel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection (ZSFOD), where no normal samples are available.
Eli Schwartz, Leonid Karlinsky, et al.
NeurIPS 2018
Jonathan H. Connell, Nalini K. Ratha, et al.
ICIP 2002
Jeffrey Heer, Adam Perer
VAST 2011
Jia Cui, Yonggang Deng, et al.
ASRU 2009