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
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CHAD: Charlotte Anomaly DatasetCode1
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly DetectorCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Diversity-Measurable Anomaly DetectionCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Do Language Models Understand Time?Code1
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly DetectionCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Anomaly Detection Requires Better RepresentationsCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-RaysCode1
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagesCode1
Dual-path Frequency Discriminators for Few-shot Anomaly DetectionCode1
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect MatchesCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Anomaly Detection under Distribution ShiftCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
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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