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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 2251–2300 of 4856 papers

TitleStatusHype
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—0
Deep Anomaly Detection under Labeling Budget ConstraintsCode0
Energy TransformerCode1
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban ScenesCode1
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework—0
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal AttentionCode1
Explainable Anomaly Detection in Images and Videos: A SurveyCode1
Deep Orthogonal Hypersphere Compression for Anomaly DetectionCode1
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory AnalysisCode0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks—0
Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly DetectionCode1
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial NetworksCode0
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep ModelsCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Unsupervised Deep Learning for IoT Time Series—0
Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study—0
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants—0
Perception Datasets for Anomaly Detection in Autonomous Driving: A SurveyCode1
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction—0
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Control—0
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting—0
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial NetworksCode0
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection—0
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent DrivingCode0
Laplacian Change Point Detection for Single and Multi-view Dynamic GraphsCode1
An optimization method for out-of-distribution anomaly detection models—0
Graph Anomaly Detection in Time Series: A Survey—0
Real-Time Outlier Detection with Dynamic Process Limits—0
LogAI: A Library for Log Analytics and IntelligenceCode2
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
Exploring Image Augmentations for Siamese Representation Learning with Chest X-RaysCode1
Approximating DTW with a convolutional neural network on EEG data—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Streaming Anomaly DetectionCode2
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore—0
Making Reconstruction-based Method Great Again for Video Anomaly DetectionCode1
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly DetectionCode1
Deep Industrial Image Anomaly Detection: A SurveyCode4
Coincident Learning for Unsupervised Anomaly Detection—0
Parkinson gait modelling from an anomaly deep representation—0
Unravelling physics beyond the standard model with classical and quantum anomaly detection—0
Quantum anomaly detection in the latent space of proton collision events at the LHCCode1
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles—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
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