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 976–1000 of 4856 papers

TitleStatusHype
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Automatic Mapping of Anatomical Landmarks from Free-Text Using Large Language Models: Insights from Llama-2—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
Automated Model Selection for Time-Series Anomaly Detection—0
Anomaly Detection for an E-commerce Pricing System—0
A Framework of Sparse Online Learning and Its Applications—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support—0
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Anomaly Detection for Network Connection Logs—0
Background subtraction on depth videos with convolutional neural networks—0
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration—0
Automated Anomaly Detection on European XFEL Klystrons—0
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework—0
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—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