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
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....
Anomaly detectionNegative selection algorithm (NSA)Clonal selection algorithm (CSA)Unsupervised dataSemi-supervised dataSYSTEMFAULTA key challenge in anomaly detection is the imbalance between the amounts of normal and abnormal signal data. Specifically, the amount of abnormal signal data is considerably ...
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...
标题:Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection 论文:https://arxiv.org/pdf/2503.19357 源码:https://github.com/farzad-bz/DeCo-Diff 概要 扩散模型的最新进展推动了其在基于重建的无监督异常检测中的应用研究。然而,这些方法在保持结构完整...
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)
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...
have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring Locality-Enhanced State Space (LSS) modules at multi-scales. The proposed LSS modu...
XIAO Zheng-hong, CHEN Zhi-gang, DENG Xiao-heng. Anomaly detection based on a multi-class CUSUM algo- rithm for WSN [ J ]. Journal of Computers, 2010,5 (2) : 306-313.XIAO Z, CHEN Z, DENG X. Anomaly detection based on a multi-class CUSUM algorithm for WSN [J].Journal of ...