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 801825 of 4856 papers

TitleStatusHype
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersCode1
Reconstruction by Inpainting for Visual Anomaly DetectionCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm RateCode1
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly DetectionCode1
Automating Outlier Detection via Meta-LearningCode1
Real-Time Anomaly Detection in Edge StreamsCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Generator Versus Segmentor: Pseudo-healthy SynthesisCode1
Understanding Coarsening for Embedding Large-Scale GraphsCode1
PySAD: A Streaming Anomaly Detection Framework in PythonCode1
Multivariate Time-series Anomaly Detection via Graph Attention NetworkCode1
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noiseCode1
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing FlowsCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile RobotsCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
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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
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (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