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 3801–3825 of 4856 papers

TitleStatusHype
Localizing Anomalies from Weakly-Labeled VideosCode1
Image quality assessment for closed-loop computer-assisted lung ultrasound—0
Using Ensemble Classifiers to Detect Incipient Anomalies—0
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation—0
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection—0
SECODA: Segmentation- and Combination-Based Detection of AnomaliesCode0
Feature Clustering for Support Identification in Extreme Regions—0
Statistical Evaluation of Anomaly Detectors for SequencesCode0
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean—0
Learning to Detect Anomalous Wireless Links in IoT Networks—0
Anomaly localization by modeling perceptual featuresCode1
Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection—0
ARCADe: A Rapid Continual Anomaly DetectorCode1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesCode1
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0—0
Interpretable Anomaly Detection with Mondrian Pólya Forests on Data Streams—0
Learning Based Methods for Traffic Matrix Estimation from Link Measurements—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Clustering Driven Deep Autoencoder for Video Anomaly Detection—0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
On the Nature and Types of Anomalies: A Review of Deviations in Data—0
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
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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