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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 21–30 of 39 papers

TitleStatusHype
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly DetectionCode3
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference—0
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly DetectionCode1
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
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