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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 901–950 of 4856 papers

TitleStatusHype
Segmentation-Based Deep-Learning Approach for Surface-Defect DetectionCode1
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
Unsupervised Traffic Accident Detection in First-Person VideosCode1
BIVA: A Very Deep Hierarchy of Latent Variables for Generative ModelingCode1
One-Class Convolutional Neural NetworkCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
PyOD: A Python Toolbox for Scalable Outlier DetectionCode1
Deep Anomaly Detection with Outlier ExposureCode1
Adversarially Learned Anomaly DetectionCode1
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop LearningCode1
Active Anomaly Detection via EnsemblesCode1
How To Backdoor Federated LearningCode1
Future Frame Prediction for Anomaly Detection – A New BaselineCode1
Deep Anomaly Detection Using Geometric TransformationsCode1
Real-world Anomaly Detection in Surveillance VideosCode1
Future Frame Prediction for Anomaly Detection -- A New BaselineCode1
MURA: Large Dataset for Abnormality Detection in Musculoskeletal RadiographsCode1
Deep and Confident Prediction for Time Series at UberCode1
Incorporating Feedback into Tree-based Anomaly DetectionCode1
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural NetworksCode1
Deep SetsCode1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksCode1
Robust random cut forest based anomaly detection on streamsCode1
Deep Structured Energy Based Models for Anomaly DetectionCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Auto-Encoding Variational BayesCode1
Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems—0
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys—0
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering—0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy—0
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection—0
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers—0
seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees—0
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23—0
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study—0
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection—0
Evaluating Language Models For Threat Detection in IoT Security LogsCode0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems—0
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection—0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark—0
E-ABIN: an Explainable module for Anomaly detection in BIological NetworksCode0
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time SeriesCode0
Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects—0
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based ModelsCode0
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology—0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—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