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 451475 of 4856 papers

TitleStatusHype
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
HSTforU: anomaly detection in aerial and ground-based videos with hierarchical spatio-temporal transformer for U-netCode1
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 codeCode1
Identify Backdoored Model in Federated Learning via Individual UnlearningCode1
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly DetectionCode1
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noiseCode1
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
Informative knowledge distillation for image anomaly segmentationCode1
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and MonitoringCode1
Inpainting Transformer for Anomaly DetectionCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Camouflaged Object DetectionCode1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Intrinsic persistent homology via density-based metric learningCode1
Can LLMs Understand Time Series Anomalies?Code1
Isolation Mondrian Forest for Batch and Online Anomaly DetectionCode1
Anomaly Detection-Based Unknown Face Presentation Attack DetectionCode1
Iterative energy-based projection on a normal data manifold for anomaly localizationCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality 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