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

Showing 101150 of 4856 papers

TitleStatusHype
Multimodal Industrial Anomaly Detection via Hybrid FusionCode2
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMCode2
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and BenchmarksCode2
ResAD: A Simple Framework for Class Generalizable Anomaly DetectionCode2
Rethinking Graph Neural Networks for Anomaly DetectionCode2
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveCode2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting MaskCode2
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
Generative Semi-supervised Graph Anomaly DetectionCode2
Streaming Anomaly DetectionCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
Tests for model misspecification in simulation-based inference: from local distortions to global model checksCode2
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly DetectionCode2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language ModelsCode2
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online AdaptationCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
FITS: Modeling Time Series with 10k ParametersCode2
GenDet: Towards Good Generalizations for AI-Generated Image DetectionCode2
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection FrameworkCode2
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised AnomalyCode2
Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing ProcessCode2
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
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
dtaianomaly: A Python library for time series anomaly detectionCode2
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable LocalizationCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly DetectionCode2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
CSAD: Unsupervised Component Segmentation for Logical Anomaly DetectionCode2
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency PatchingCode2
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly DetectionCode2
European Space Agency Benchmark for Anomaly Detection in Satellite TelemetryCode2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted FeaturesCode2
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified