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 1076–1100 of 4856 papers

TitleStatusHype
Auditing Keyword Queries Over Text Documents—0
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents—0
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description—0
Anomaly Detection Based on Critical Paths for Deep Neural Networks—0
A Typology of Data Anomalies—0
Anomaly Detection Based on Aggregation of Indicators—0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security—0
Attention to Patterns is all you need for Insider threat detection—0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs—0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making—0
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study—0
Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches—0
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder—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