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

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Showing 901–925 of 4856 papers

TitleStatusHype
Time-Series Anomaly Detection Service at MicrosoftCode1
Time Series Anomaly Detection using Diffusion-based ModelsCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
TimeSeriesExam: A time series understanding examCode1
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly DetectionCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Anomaly Detection in Aerial Videos with TransformersCode1
Explainable Anomaly Detection in Images and Videos: A SurveyCode1
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and EvaluationCode1
An Attribute-based Method for Video Anomaly DetectionCode1
Learning a Cross-modality Anomaly Detector for Remote Sensing ImageryCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
Towards Universal Unsupervised Anomaly Detection in Medical ImagingCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionsCode1
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-SupervisionCode1
Classification-Based Anomaly Detection for General DataCode1
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
GAD-NR: Graph Anomaly Detection via Neighborhood ReconstructionCode1
Neural Fourier Modelling: A Highly Compact Approach to Time-Series AnalysisCode1
T-Rep: Representation Learning for Time Series using Time-EmbeddingsCode1
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly DetectionCode1
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
Zero-Shot Anomaly Detection via Batch NormalizationCode1
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified