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 31–39 of 39 papers

TitleStatusHype
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
Show:102550
← PrevPage 4 of 4Next →

No leaderboard results yet.