论文标题
Maeay：Mae很少，零拍摄异常检测
MAEDAY: MAE for few and zero shot AnomalY-Detection
论文作者
论文摘要
我们建议使用蒙版自动编码器（MAE），这是一种在图像插入的自动训练的变压器模型，以进行异常检测（AD）。假设与正常区域相比，异常区域更难重建。 MAEDAY是第一个基于图像重建的异常检测方法，该方法利用了预训练的模型，可用于几次射击异常检测（FSAD）。我们还显示出相同的方法在没有正常样品的情况下，对于零击AD（ZSAD）和零摄像的异物检测（ZSFOD）的新任务非常出色。代码可在https://github.com/elischwartz/maeday上找到。
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 .
