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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2701–2725 of 4856 papers

TitleStatusHype
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients—0
Interruptions detection in video conferencesCode0
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection—0
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical SystemsCode0
Explainable Contextual Anomaly Detection using Quantile Regression ForestsCode0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
CNTS: Cooperative Network for Time SeriesCode0
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection—0
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
A method for incremental discovery of financial event types based on anomaly detection—0
Ultrafast single-channel machine vision based on neuro-inspired photonic computing—0
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—0
Deep Anomaly Detection under Labeling Budget ConstraintsCode0
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework—0
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory AnalysisCode0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial NetworksCode0
Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks—0
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Unsupervised Deep Learning for IoT Time Series—0
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants—0
Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study—0
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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