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 16761700 of 4856 papers

TitleStatusHype
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling0
MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic0
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly ScoresCode1
Understanding Parameter Saliency via Extreme Value Theory0
OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach0
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Towards Self-Interpretable Graph-Level Anomaly DetectionCode1
GADY: Unsupervised Anomaly Detection on Dynamic Graphs0
On Pixel-level Performance Assessment in Anomaly Detection0
Localizing Anomalies in Critical Infrastructure using Model-Based Drift ExplanationsCode0
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments0
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency0
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference0
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots0
An Event based Prediction Suffix Tree0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning0
FLTracer: Accurate Poisoning Attack Provenance in Federated LearningCode1
Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches0
Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches0
SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation0
A New Time Series Similarity Measure and Its Smart Grid Applications0
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
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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