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 101–150 of 4856 papers

TitleStatusHype
Large language models can be zero-shot anomaly detectors for time series?Code2
Rethinking Graph Neural Networks for Anomaly DetectionCode2
Is Space-Time Attention All You Need for Video Understanding?Code2
A Survey on Diffusion Models for Anomaly DetectionCode2
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly DetectionCode2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
SoftPatch+: Fully Unsupervised Anomaly Classification and SegmentationCode2
Learning to Detect Multi-class Anomalies with Just One Normal Image PromptCode2
TAB: Unified Benchmarking of Time Series Anomaly Detection MethodsCode2
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and BenchmarksCode2
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly DetectionCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection ModelsCode2
GenDet: Towards Good Generalizations for AI-Generated Image DetectionCode2
Generative Semi-supervised Graph Anomaly DetectionCode2
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMCode2
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly DetectionCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised AnomalyCode2
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly DetectionCode2
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly DetectionCode2
Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly DetectionCode2
Few-Shot Anomaly-Driven Generation for Anomaly Classification and SegmentationCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable LocalizationCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
CSAD: Unsupervised Component Segmentation for Logical Anomaly DetectionCode2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly DetectionCode2
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly DetectionCode2
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational AutoencoderCode2
dtaianomaly: A Python library for time series anomaly detectionCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency PatchingCode2
European Space Agency Benchmark for Anomaly Detection in Satellite TelemetryCode2
FITS: Modeling Time Series with 10k ParametersCode2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language ModelsCode2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted FeaturesCode2
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly DetectionCode2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
Log-based Anomaly Detection with Deep Learning: How Far Are We?Code2
Show:102550
← PrevPage 3 of 98Next →
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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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