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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 4856 papers

TitleStatusHype
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring—0
Hypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection—0
LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis—0
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak RebatesCode0
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly DetectionCode2
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple RemedyCode0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
SIGMA: Single Interpolated Generative Model for Anomalies—0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
Attention to Patterns is all you need for Insider threat detection—0
ResAD: A Simple Framework for Class Generalizable Anomaly DetectionCode2
Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study—0
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns—0
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection—0
Context-Aware Trajectory Anomaly Detection—0
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
Graph Pre-Training Models Are Strong Anomaly Detectors—0
Multi-scale feature reconstruction network for industrial anomaly detectionCode1
Coniferest: a complete active anomaly detection frameworkCode1
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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