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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3101–3125 of 4856 papers

TitleStatusHype
WiFi Motion Detection: A Study into Efficacy and Classification—0
Wireless Sensor Networks anomaly detection using Machine Learning: A Survey—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
World Models for Anomaly Detection during Model-Based Reinforcement Learning Inference—0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing—0
WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
XAI-guided Insulator Anomaly Detection for Imbalanced Datasets—0
X-MAN: Explaining multiple sources of anomalies in video—0
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
You May Need both Good-GAN and Bad-GAN for Anomaly Detection—0
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray—0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions—0
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection—0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
1D Convolutional Neural Networks and Applications: A Survey—0
Anomaly Detection in Images—0
An Adaptive Training-less System for Anomaly Detection in Crowd Scenes—0
Show:102550
← PrevPage 125 of 195Next →
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