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 1376–1400 of 4856 papers

TitleStatusHype
Self-Supervised Learning for Detecting AI-Generated Faces as AnomaliesCode0
Multivariate Time Series Anomaly Detection using DiffGAN ModelCode0
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection—0
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction—0
Training Medical Large Vision-Language Models with Abnormal-Aware Feedback—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization—0
Unseen Visual Anomaly Generation—0
PIAD: Pose and Illumination agnostic Anomaly Detection—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection—0
Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory—0
OralXrays-9: Towards Hospital-Scale Panoramic X-ray Anomaly Detection via Personalized Multi-Object Query-Aware Mining—0
TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model—0
Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and HarmonizationCode0
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Machine Learning-Based Security Policy Analysis—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—0
A Survey on Time-Series Distance Measures—0
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video 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