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 1951–2000 of 4856 papers

TitleStatusHype
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality—0
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Restricted Generative Projection for One-Class Classification and Anomaly Detection—0
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT SystemsCode1
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection MethodCode0
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
FITS: Modeling Time Series with 10k ParametersCode2
Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization—0
Contextual Affinity Distillation for Image Anomaly Detection—0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements—0
Prototypes as Explanation for Time Series Anomaly Detection—0
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis—0
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion RepresentationsCode1
Application of MUSIC-type imaging for anomaly detection without background information—0
Graph-level Anomaly Detection via Hierarchical Memory NetworksCode1
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly DetectionCode1
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images—0
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrogramsCode0
Morse Neural Networks for Uncertainty Quantification—0
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera ViewsCode0
Graph Neural Networks based Log Anomaly Detection and ExplanationCode1
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning—0
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting—0
A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact—0
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Learning normal asymmetry representations for homologous brain structuresCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Precursor-of-Anomaly Detection for Irregular Time SeriesCode1
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly DetectionCode1
Anomaly Detection with Score Distribution DiscriminationCode1
Autoencoders for Real-Time SUEP Detection—0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center—0
Triggering Dark Showers with Conditional Dual Auto-EncodersCode0
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly DetectionCode1
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly DetectorsCode1
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder—0
BMAD: Benchmarks for Medical Anomaly DetectionCode1
Machine Learning for Real-Time Anomaly Detection in Optical Networks—0
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach—0
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public ProcurementCode1
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
Towards exploring adversarial learning for anomaly detection in complex driving scenes—0
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection—0
Tailoring Machine Learning for Process Mining—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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