SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 2001–2025 of 4856 papers

TitleStatusHype
Time topological analysis of EEG using signature theory—0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
Test Time Training for Industrial Anomaly Segmentation—0
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly DetectionCode0
Learning with errors based dynamic encryption that discloses residue signal for anomaly detection—0
Foundation Models for Structural Health MonitoringCode0
Transfer learning applications for anomaly detection in wind turbines—0
Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning—0
Harnessing Large Language Models for Training-free Video Anomaly Detection—0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—0
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security—0
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems—0
On the True Distribution Approximation of Minimum Bayes-Risk DecodingCode0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
Long-Tailed Anomaly Detection with Learnable Class Names—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Road Obstacle Detection based on Unknown Objectness Scores—0
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks—0
Few-shot Online Anomaly Detection and SegmentationCode0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified