MAEDAY: MAE for few and zero shot AnomalY-Detection
- URL: http://arxiv.org/abs/2211.14307v2
- Date: Thu, 15 Feb 2024 15:39:40 GMT
- Title: MAEDAY: MAE for few and zero shot AnomalY-Detection
- Authors: Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian
Scheidegger, Sivan Doveh, Raja Giryes
- Abstract summary: We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD)
MaEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model.
- Score: 44.99483220711847
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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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