论文标题
U-Flow：U形归一流的流动，用于无监督阈值异常检测
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
论文作者
论文摘要
在这项工作中，我们提出了一种单级自我监督的方法，用于在图像中分割异常，从而受益于现代机器学习方法和更经典的统计检测理论。该方法由四个阶段组成。首先，使用多尺度图像变压器体系结构提取功能。然后，将这些特征馈入U形归一流流量（NF），该流量为后续阶段奠定了理论基础。第三阶段从NF嵌入计算像素级异常图，最后一阶段根据A逆转录框架进行分割。这种多个假设测试策略允许推导可靠的无监督检测阈值，这在需要操作点的现实应用程序中至关重要。使用联合（MIOU）度量的平均交叉点评估分割结果，并且为了评估生成的异常图，我们报告了接收器工作特征曲线（AUROC）以及各个区域跨层曲线（AUPRO）下的面积。在各种数据集中进行的广泛实验表明，所提出的方法为所有指标和所有数据集产生最先进的结果，在大多数MVTEC-AD类别中排名第一，平均像素级AUROC为98.74％。可以在https：// github.com/mtailanian/uflow上找到代码和训练有素的模型。
In this work we propose a one-class self-supervised method for anomaly segmentation in images that benefits both from a modern machine learning approach and a more classic statistical detection theory. The method consists of four phases. First, features are extracted using a multi-scale image Transformer architecture. Then, these features are fed into a U-shaped Normalizing Flow (NF) that lays the theoretical foundations for the subsequent phases. The third phase computes a pixel-level anomaly map from the NF embedding, and the last phase performs a segmentation based on the a contrario framework. This multiple hypothesis testing strategy permits the derivation of robust unsupervised detection thresholds, which are crucial in real-world applications where an operational point is needed. The segmentation results are evaluated using the Mean Intersection over Union (mIoU) metric, and for assessing the generated anomaly maps we report the area under the Receiver Operating Characteristic curve (AUROC), as well as the Area Under the Per-Region-Overlap curve (AUPRO). Extensive experimentation in various datasets shows that the proposed approach produces state-of-the-art results for all metrics and all datasets, ranking first in most MVTec-AD categories, with a mean pixel-level AUROC of 98.74%. Code and trained models are available at https:// github.com/mtailanian/uflow.
