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 1851–1875 of 4856 papers

TitleStatusHype
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks—0
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets—0
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams—0
On the Effectiveness of Log Representation for Log-based Anomaly DetectionCode1
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly DetectionCode0
Detecting Financial Market Manipulation with Statistical Physics Tools—0
Interpretable Online Log Analysis Using Large Language Models with Prompt StrategiesCode1
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection—0
Future Video Prediction from a Single Frame for Video Anomaly Detection—0
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced RecognitionCode1
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection—0
Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless NetworksCode0
Survey on video anomaly detection in dynamic scenes with moving cameras—0
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch RetrievalCode1
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
Out-of-Distribution Detection for Monocular Depth EstimationCode1
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless FunctionsCode0
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving—0
Deep generative models for unsupervised delamination detection using guided wavesCode0
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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