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 .
@article{arxiv.2211.14307,
title = {MAEDAY: MAE for few and zero shot AnomalY-Detection},
author = {Eli Schwartz and Assaf Arbelle and Leonid Karlinsky and Sivan Harary and Florian Scheidegger and Sivan Doveh and Raja Giryes},
journal= {arXiv preprint arXiv:2211.14307},
year = {2024}
}