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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