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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
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
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
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