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
ALFA: A Dataset for UAV Fault and Anomaly DetectionCode0
Exploring Deep Anomaly Detection Methods Based on Capsule NetCode0
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware DetectorCode0
Extended Isolation ForestCode0
Explaining Anomalies in Groups with Characterizing Subspace RulesCode0
Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly DetectionCode0
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densitiesCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Explainable Machine Learning for Cyberattack Identification from Traffic FlowsCode0
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing MonitoringCode0
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time SeriesCode0
FABLE : Fabric Anomaly Detection Automation ProcessCode0
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly DetectionCode0
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learningCode0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly DetectionCode0
Exact Optimization of Conformal Predictors via Incremental and Decremental LearningCode0
Enhancing Interpretability and Generalizability in Extended Isolation ForestsCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETCode0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CTCode0
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural NetworksCode0
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining TasksCode0
Evaluating Vision Transformer Models for Visual Quality Control in Industrial ManufacturingCode0
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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