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 2301–2325 of 4856 papers

TitleStatusHype
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning—0
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing—0
A convolutional neural network of low complexity for tumor anomaly detection—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation—0
Condition monitoring and anomaly detection in cyber-physical systems—0
Position Regression for Unsupervised Anomaly DetectionCode0
Hybrid Open-set Segmentation with Synthetic Negative DataCode1
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection—0
The role of noise in denoising models for anomaly detection in medical imagesCode1
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
FewSOME: One-Class Few Shot Anomaly Detection with Siamese NetworksCode1
Subgraph Centralization: A Necessary Step for Graph Anomaly DetectionCode1
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
On Advantages of Mask-level Recognition for Outlier-aware SegmentationCode1
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
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions—0
Self-Supervised Video Forensics by Audio-Visual Anomaly DetectionCode1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
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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