Multi-class Anomaly Detection is a task that identifies anomalies by jointly learning and detecting outliers across multiple classes, in contrast to traditional Anomaly Detection, which typically focuses on identifying anomalies within a single class....
A unified model for multi-class anomaly detection 1 Introduction 现有方法[6, 11, 25, 27, 48, 49, 52]建议为不同类别的对象训练单独的模型,就像图1c中的情况一样。然而,这种一类一模型的方案可能会消耗大量内存,尤其是随着类别数量的增加,并且不适用于正常样本表现出较大的类内多样性的场景(即一个对象...
传统的无监督异常检测(Unsupervised Anomaly Detection, UAD)方法通常假设正常样本具有一致的模式,而异常样本则偏离这些模式。然而,在多类异常检测中,正常样本可能来自多个不同的类别,具有多样化的模式。这种多样性可能导致模型在面对未见过的模式时,倾向于将其泛化为正常样本,从而无法有效检测异常。这种现象被称为“恒等捷...
因此,由于我们的方法是专门为解决 "雷同捷径 "问题而设计的,我们的方法在统一情况下也是有效的。 3.2 为统一的异常检测改进特征重构(Improving feature reconstruction for unified anomaly detection) 概述。如图3所示,UniAD是由一个邻居掩码编码器(NME)和一个分层查询解码器(LQD)组成。首先,由固定的预训练骨干网络提...
Based on our experiments, the model we proposed has the ability to decrease False Positive Rates and enhance the performance of anomaly-based IDSs.Darko, Michael A.Engineering,Computer
The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (CAD) to dete...
Unsupervised anomaly detection techniques, which do not rely on prior knowledge of anomalies, have attracted considerable attention in the field of industrial surface inspection. However, existing approaches commonly employ separate models for each product class, resulting in substantial storage requirements ...
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection Haoyang He1#,Jiangning Zhang1,2#,Hongxu Chen1,Xuhai Chen1,Zhishan Li1,Xu Chen2,Yabiao Wang2,Chengjie Wang2,Lei Xie1* (#Equal contribution, *Corresponding author)
3.2.1. Multi-class anomaly detection-inspired segmentation As demonstrated in Hansen et al. (2022), an anomaly detection-inspired approach to few-shot medical image segmentation results in a model that is less sensitive to variations in the background class, thus enabling one-step volume-wise 3D...
Xiao Zhenghong; Chen Zhigang; Deng Xiaoheng.Anomaly detection based on a multi-class CUSUM algorithm for WSN.Joumal of Computers.2010.306-313XIAO Zheng-hong, CHEN Zhi-gang, DENG Xiao-heng. Anomaly detection based on a multi-class CUSUM algo- rithm for WSN [ J ]. Journal of Computers, ...