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 76100 of 4856 papers

TitleStatusHype
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable LocalizationCode2
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
dtaianomaly: A Python library for time series anomaly detectionCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly DetectionCode2
FITS: Modeling Time Series with 10k ParametersCode2
European Space Agency Benchmark for Anomaly Detection in Satellite TelemetryCode2
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
Few-Shot Anomaly-Driven Generation for Anomaly Classification and SegmentationCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language ModelsCode2
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted FeaturesCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly DetectionCode2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic ThresholdingCode2
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly DetectionCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational AutoencoderCode2
Anomaly Transformer: Time Series Anomaly Detection with Association DiscrepancyCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency PatchingCode2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
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Benchmark Results

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