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 2051–2075 of 4856 papers

TitleStatusHype
Efficient Anomaly Detection via Matrix Sketching—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System—0
Efficient anomaly detection using bipartite k-NN graphs—0
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching—0
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications—0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches—0
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Attack-Agnostic Adversarial Detection—0
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data—0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Anomaly Detection and Localisation using Mixed Graphical Models—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
Active Anomaly Detection with Switching Cost—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs—0
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series—0
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection—0
Atom dimension adaptation for infinite set dictionary learning—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