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

Showing 23762400 of 4856 papers

TitleStatusHype
Cross-Domain Video Anomaly Detection without Target Domain Adaptation0
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view informationCode0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series0
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Transformer-based normative modelling for anomaly detection of early schizophrenia0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection0
On Root Cause Localization and Anomaly Mitigation through Causal InferenceCode1
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection0
Prototypical Residual Networks for Anomaly Detection and Localization0
Continual learning on deployment pipelines for Machine Learning Systems0
Anomaly Detection in Power Markets and Systems0
AIDA: Analytic Isolation and Distance-based Anomaly Detection AlgorithmCode0
FEMa-FS: Finite Element Machines for Feature Selection0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems0
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection0
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View0
An Attribute-based Method for Video Anomaly DetectionCode1
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch0
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis InitiativeCode0
Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial PerturbationsCode0
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified