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 15261550 of 4856 papers

TitleStatusHype
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection0
Interpretable Measurement of CNN Deep Feature Density using Copula and the Generalized Characteristic Function0
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound ImagingCode0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers0
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches0
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel ApproachCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
Anomalous Client Detection in Federated Learning0
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models0
Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions0
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal PropertiesCode0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation0
Directional anomaly detection0
Partial Channel Dependence with Channel Masks for Time Series Foundation ModelsCode0
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly DetectorCode0
LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis0
Hypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection0
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak RebatesCode0
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
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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