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 1576–1600 of 4856 papers

TitleStatusHype
Detecting Anomalies using Generative Adversarial Networks on Images—0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—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
Anomize: Better Open Vocabulary Video Anomaly Detection—0
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle—0
Detecting Anomalous User Behavior in Remote Patient Monitoring—0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data—0
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing—0
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions—0
Detecting Contextual Network Anomalies with Graph Neural Networks—0
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noise—0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Detecting fake accounts through Generative Adversarial Network in online social media—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
Detecting Financial Market Manipulation with Statistical Physics Tools—0
Detecting Gait Abnormalities in Foot-Floor Contacts During Walking Through Footstep-Induced Structural Vibrations—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—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