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 1776–1800 of 4856 papers

TitleStatusHype
Anomaly Detection for Network Connection Logs—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees—0
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Anomaly Detection for Industrial Big Data—0
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection—0
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review—0
AGAD: Adversarial Generative Anomaly Detection—0
Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning—0
Anomaly Detection for Incident Response at Scale—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Automatic Mapping of Anatomical Landmarks from Free-Text Using Large Language Models: Insights from Llama-2—0
Anomaly Detection for imbalanced datasets with Deep Generative Models—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants—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