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 2026–2050 of 4856 papers

TitleStatusHype
Extracting Information from Indian First Names—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation—0
Foundation Models for Anomaly Detection: Vision and Challenges—0
Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data—0
Cross-Domain Video Anomaly Detection without Target Domain Adaptation—0
Cross-Domain Learning for Video Anomaly Detection with Limited Supervision—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
Anomaly Detection with Joint Representation Learning of Content and Connection—0
Fair Anomaly Detection For Imbalanced Groups—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
Faithful Explanations for Deep Graph Models—0
Anomaly Detection with Inexact Labels—0
A geometric framework for outlier detection in high-dimensional data—0
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network—0
Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection—0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection—0
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos—0
Anomaly Detection with Generative Adversarial Networks—0
Feature Extraction for Novelty Detection in Network Traffic—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