  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. Code is available at
https://github.com/EliSchwartz/MAEDAY .
