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 3426–3450 of 4856 papers

TitleStatusHype
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit AnomaliesCode0
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction—0
A2Log: Attentive Augmented Log Anomaly Detection—0
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion DetectionCode0
Anomaly Detection in Radar Data Using PointNets—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI—0
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection—0
LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection—0
A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization—0
Enhancing Unsupervised Anomaly Detection with Score-Guided NetworkCode0
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems—0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation—0
gen2Out: Detecting and Ranking Generalized AnomaliesCode0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method—0
Computing Graph Descriptors on Edge Streams—0
In-body Bionanosensor Localization for Anomaly Detection via Inertial Positioning and THz Backscattering Communication—0
Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative ModelsCode0
Show:102550
← PrevPage 138 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