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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
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
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
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly DetectionCode0
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly DetectionCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD BenchmarkCode0
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