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 1551–1575 of 4856 papers

TitleStatusHype
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple RemedyCode0
SIGMA: Single Interpolated Generative Model for Anomalies—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Attention to Patterns is all you need for Insider threat detection—0
Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study—0
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns—0
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection—0
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis—0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Context-Aware Trajectory Anomaly Detection—0
Graph Pre-Training Models Are Strong Anomaly Detectors—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis—0
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast—0
Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly DetectionCode0
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling—0
Spatio-temporal Multivariate Cluster Evolution Analysis for Detecting and Tracking Climate ImpactsCode0
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—0
XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving SystemsCode0
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing—0
ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life—0
Towards Unsupervised Validation of Anomaly-Detection Models—0
A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks—0
Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis—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