论文标题
补丁SVDD：用于异常检测和分割的补丁级SVDD
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
论文作者
论文摘要
在本文中，我们解决了图像异常检测和分割的问题。异常检测涉及对输入图像是否包含异常做出二进制决策，而异常分割旨在在像素水平上定位异常。支持向量数据描述（SVDD）是一种用于异常检测的长期算法，我们使用自我监督的学习将其深度学习变体扩展到基于斑块的方法。该扩展可以使异常分割并改善检测性能。结果，与先前的最新方法相比，在AUROC中测得的AUROC中的异常检测和分割性能分别增加了9.8％和7.0％。我们的结果表明该方法的功效及其对工业应用的潜力。对拟议方法的详细分析提供了有关其行为的见解，并且该代码可在线获得。
In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algorithm used for an anomaly detection, and we extend its deep learning variant to the patch-based method using self-supervised learning. This extension enables anomaly segmentation and improves detection performance. As a result, anomaly detection and segmentation performances measured in AUROC on MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the previous state-of-the-art methods. Our results indicate the efficacy of the proposed method and its potential for industrial application. Detailed analysis of the proposed method offers insights regarding its behavior, and the code is available online.
