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 2201–2225 of 4856 papers

TitleStatusHype
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection—0
Graph-Level Embedding for Time-Evolving Graphs—0
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss—0
Condition monitoring and anomaly detection in cyber-physical systems—0
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach—0
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model—0
Anomaly Detection Under Multiplicative Noise Model Uncertainty—0
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network—0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection—0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Computing Graph Descriptors on Edge Streams—0
AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN—0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey—0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
Anomaly detection through latent space restoration using vector-quantized variational autoencoders—0
A Massively Parallel Associative Memory Based on Sparse Neural Networks—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