SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 39 papers

TitleStatusHype
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
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD BenchmarkCode0
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
Show:102550
← PrevPage 3 of 4Next →

No leaderboard results yet.