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 2751–2800 of 4856 papers

TitleStatusHype
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
Proximally Sensitive Error for Anomaly Detection and Feature Learning—0
Attack-Agnostic Adversarial Detection—0
MAD-EN: Microarchitectural Attack Detection through System-wide Energy ConsumptionCode0
Rethinking Graph Neural Networks for Anomaly DetectionCode2
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in VideosCode0
Benchmarking Unsupervised Anomaly Detection and Localization—0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection—0
Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network—0
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty DetectionCode1
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
PSL is Dead. Long Live PSL—0
Raising the Bar in Graph-level Anomaly DetectionCode1
Are Transformers Effective for Time Series Forecasting?Code4
Attention-based residual autoencoder for video anomaly detectionCode1
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Faithful Explanations for Deep Graph Models—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines—0
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier ImagesCode1
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition—0
PAC-Wrap: Semi-Supervised PAC Anomaly Detection—0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTMCode0
Automating In-Network Machine LearningCode1
Unsupervised Features Ranking via Coalitional Game Theory for Categorical DataCode0
Federated Anomaly Detection over Distributed Data Streams—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest RadiographsCode1
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature—0
Anomaly Detection in Intra-Vehicle Networks—0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding—0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training—0
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time SeriesCode1
A Contrario multi-scale anomaly detection method for industrial quality inspection—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