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 2051–2100 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
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
EdgeCentric: Anomaly Detection in Edge-Attributed Networks—0
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—0
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph—0
ECNN: A Low-complex, Adjustable CNN for Industrial Pump Monitoring Using Vibration Data—0
Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications—0
ECG classification using Deep CNN and Gramian Angular Field—0
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure—0
ECATS: Explainable-by-design concept-based anomaly detection for time series—0
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection—0
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments—0
Anomaly Detection and Inlet Pressure Prediction in Water Distribution Systems Using Machine Learning—0
EasyNet: An Easy Network for 3D Industrial Anomaly Detection—0
Early Warning Signals of Social Instabilities in Twitter Data—0
Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model—0
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels—0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Abnormal Client Behavior Detection in Federated Learning—0
Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors—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