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 2376–2400 of 4856 papers

TitleStatusHype
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A New Time Series Similarity Measure and Its Smart Grid Applications—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis—0
Detecting Anomalous User Behavior in Remote Patient Monitoring—0
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle—0
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics—0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
Detecting Anomalies Using Rotated Isolation Forest—0
Detecting Anomalies using Generative Adversarial Networks on Images—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
ADTR: Anomaly Detection Transformer with Feature Reconstruction—0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis—0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection—0
Detecting Anomalies from Video-Sequences: a Novel Descriptor—0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM—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