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arXiv · 2211.14307

MAEDAY: MAE for few and zero shot AnomalY-Detection

Eli Schwartz·Assaf Arbelle·Leonid Karlinsky·Sivan Harary·Florian Scheidegger·Sivan Doveh·Raja Giryes
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Abstract

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 .

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Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian Scheidegger, Sivan Doveh, Raja Giryes. 2022-11-25. MAEDAY: MAE for few and zero shot AnomalY-Detection. https://arxiv.org/abs/2211.14307

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