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 2351–2400 of 4856 papers

TitleStatusHype
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Mesh of Things (MoT) Network-Driven Anomaly Detection in Connected Objects—0
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT NetworksCode1
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)—0
SiteFerret: beyond simple pocket identification in proteinsCode1
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections—0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
Continual Learning Approaches for Anomaly DetectionCode0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
Resonant Anomaly Detection with Multiple Reference Datasets—0
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos—0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network—0
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks—0
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
CHAD: Charlotte Anomaly DatasetCode1
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance—0
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Anomaly Detection in Driving by Cluster Analysis Twice—0
EVAL: Explainable Video Anomaly Localization—0
A scalable framework for annotating photovoltaic cell defects in electroluminescence images—0
Lorentz group equivariant autoencodersCode1
Cross-Domain Video Anomaly Detection without Target Domain Adaptation—0
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 Series—0
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Transformer-based normative modelling for anomaly detection of early schizophrenia—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
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 Methods—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
Continual learning on deployment pipelines for Machine Learning Systems—0
Anomaly Detection in Power Markets and Systems—0
AIDA: Analytic Isolation and Distance-based Anomaly Detection AlgorithmCode0
FEMa-FS: Finite Element Machines for Feature Selection—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View—0
An Attribute-based Method for Video Anomaly DetectionCode1
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning—0
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch—0
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 Data—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
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