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 426450 of 4856 papers

TitleStatusHype
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection0
Foundation Models for Anomaly Detection: Vision and Challenges0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection0
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes0
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised AnomalyCode2
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder0
Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions0
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks0
Position: Untrained Machine Learning for Anomaly Detection0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach0
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping0
NLP-Based .NET CLR Event Logs AnalyzerCode0
SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited LabelsCode1
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making0
TopoCL: Topological Contrastive Learning for Time Series0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition0
Anomaly Detection via Autoencoder Composite Features and NCE0
LAST SToP For Modeling Asynchronous Time Series0
A Poisson Process AutoDecoder for X-ray Sources0
ConditionNET: Learning Preconditions and Effects for Execution Monitoring0
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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