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 601625 of 4856 papers

TitleStatusHype
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Task-oriented Self-supervised Learning for Anomaly Detection in ElectroencephalographyCode1
Multivariate Time Series Anomaly Detection with Few Positive SamplesCode1
Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devicesCode1
Local Evaluation of Time Series Anomaly Detection AlgorithmsCode1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and SegmentationCode1
Multi Visual Modality Fall Detection DatasetCode1
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-raysCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Deep Isolation Forest for Anomaly DetectionCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data StreamCode1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-RaysCode1
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)Code1
Position Encoding Enhanced Feature Mapping for Image Anomaly DetectionCode1
Anomaly detection in surveillance videos using transformer based attention modelCode1
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty DetectionCode1
Raising the Bar in Graph-level Anomaly DetectionCode1
Attention-based residual autoencoder for video anomaly detectionCode1
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier ImagesCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Automating In-Network Machine LearningCode1
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest RadiographsCode1
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time SeriesCode1
Multimodal Detection of Unknown Objects on Roads for Autonomous DrivingCode1
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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