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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 526–550 of 4856 papers

TitleStatusHype
HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-netCode1
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction—0
Multivariate Time Series Anomaly Detection using DiffGAN ModelCode0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
Training Medical Large Vision-Language Models with Abnormal-Aware Feedback—0
TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model—0
PIAD: Pose and Illumination agnostic Anomaly Detection—0
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization—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
Unseen Visual Anomaly Generation—0
Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory—0
DFM: Differentiable Feature Matching for Anomaly Detection—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and HarmonizationCode0
Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline—0
OralXrays-9: Towards Hospital-Scale Panoramic X-ray Anomaly Detection via Personalized Multi-Object Query-Aware Mining—0
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
SoftPatch+: Fully Unsupervised Anomaly Classification and SegmentationCode2
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—0
Dive into Time-Series Anomaly Detection: A Decade ReviewCode0
Show:102550
← PrevPage 22 of 195Next →
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