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 2451–2475 of 4856 papers

TitleStatusHype
Discriminative Graph-level Anomaly Detection via Dual-students-teacher ModelCode0
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomaliesCode0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Patch-wise Auto-Encoder for Visual Anomaly Detection—0
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain—0
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection—0
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection SystemCode0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping—0
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series InputCode0
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis—0
EasyNet: An Easy Network for 3D Industrial Anomaly Detection—0
Coupled-Space Attacks against Random-Walk-based Anomaly DetectionCode0
ECG classification using Deep CNN and Gramian Angular Field—0
Sobolev Space Regularised Pre Density Models—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models—0
Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—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