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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
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
Explainable multi-class anomaly detection on functional data—0
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection—0
Multi-Class Anomaly Detection—0
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization—0
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference—0
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
mixed attention auto encoder for multi-class industrial anomaly detection—0
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