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 476500 of 4856 papers

TitleStatusHype
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Laplacian Change Point Detection for Dynamic GraphsCode1
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANsCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Learning Decision Trees as Amortized Structure InferenceCode1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and SegmentationCode1
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentCode1
Learning Memory-guided Normality for Anomaly DetectionCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
Learning Neural Set Functions Under the Optimal Subset OracleCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
CHAD: Charlotte Anomaly DatasetCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Camouflaged Object DetectionCode1
Locally Masked Convolution for Autoregressive ModelsCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Can LLMs Understand Time Series Anomalies?Code1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly DetectorCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Complementary Pseudo Multimodal Feature for Point Cloud 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