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Multi-class Anomaly Detection

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.

Papers

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Showing 26–39 of 39 papers

TitleStatusHype
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly DetectionCode1
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly DetectionCode0
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
mixed attention auto encoder for multi-class industrial anomaly detection—0
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly DetectionCode1
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
A Unified Model for Multi-class Anomaly DetectionCode2
Explainable multi-class anomaly detection on functional data—0
Multi-Class Anomaly Detection—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
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