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 2751–2775 of 4856 papers

TitleStatusHype
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
Position Regression for Unsupervised Anomaly DetectionCode0
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Anomalies, Representations, and Self-Supervision—0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing—0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model—0
A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data—0
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Feature Prediction Diffusion Model for Video Anomaly Detection—0
A principled distributional approach to trajectory similarity measurement—0
Template-guided Hierarchical Feature Restoration for Anomaly Detection—0
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks—0
Application Of ADNN For Background Subtraction In Smart Surveillance System—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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