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 951–975 of 4856 papers

TitleStatusHype
Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering—0
Hi-SAM: A high-scalable authentication model for satellite-ground Zero-Trust system using mean field game—0
A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs—0
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts—0
What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised HorizonCode0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Cross-Domain Learning for Video Anomaly Detection with Limited Supervision—0
Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity LevelsCode0
The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era—0
Cluster-Wide Task Slowdown Detection in Cloud SystemCode0
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection—0
Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon—0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive LearningCode0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions—0
Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data—0
Can LLMs Serve As Time Series Anomaly Detectors?—0
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect DetectionCode9
CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly DetectionCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
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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