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 4226–4250 of 4856 papers

TitleStatusHype
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
Detecting Log Anomalies with Multi-Head Attention (LAMA)—0
Detecting Novelties with Empty Classes—0
Detecting out-of-context objects using contextual cues—0
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models—0
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Detecting Relative Anomaly—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach—0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning—0
Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code—0
Detection and Statistical Modeling of Birth-Death Anomaly—0
D\'etection automatique d'anomalies sur deux styles de parole dysarthrique: parole lue vs spontan\'ee (Automatic anomaly detection for dysarthria across two speech styles : read vs spontaneous speech)—0
Show:102550
← PrevPage 170 of 195Next →
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