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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 11–20 of 39 papers

TitleStatusHype
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector QuantizationCode1
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection—0
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly DetectionCode0
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly DetectionCode3
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD BenchmarkCode0
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