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 1826–1850 of 4856 papers

TitleStatusHype
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks—0
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks—0
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures—0
SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning—0
Autoencoders for Semivisible Jet Detection—0
Autoencoders for Real-Time SUEP Detection—0
Autoencoders for Anomaly Detection are Unreliable—0
Anomaly Detection by One Class Latent Regularized Networks—0
A framework for anomaly detection using language modeling, and its applications to finance—0
AutoEncoder Convolutional Neural Network for Pneumonia Detection—0
Anomaly Detection by Context Contrasting—0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter—0
Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations—0
Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss—0
Activity report analysis with automatic single or multispan answer extraction—0
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines—0
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter—0
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
A flexible outlier detector based on a topology given by graph communities—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