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 4126–4150 of 4856 papers

TitleStatusHype
Online Adaptive Asymmetric Active Learning with Limited BudgetsCode0
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models—0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security—0
Real-time Anomaly Detection and Classification in Streaming PMU Data—0
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Anomaly Detection in Large Scale Networks with Latent Space Models—0
Uncertainty on Asynchronous Time Event PredictionCode0
Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Deep Variational Semi-Supervised Novelty Detection—0
RAD: On-line Anomaly Detection for Highly Unreliable Data—0
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network—0
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent EmbeddingsCode0
Convolutional Neural Network for Multipath Detection in GNSS Receivers—0
Dynamic Graph Embedding via LSTM History Tracking—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors—0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type DataCode0
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)—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