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 4351–4400 of 4856 papers

TitleStatusHype
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors—0
Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model—0
Early Warning Signals of Social Instabilities in Twitter Data—0
EasyNet: An Easy Network for 3D Industrial Anomaly Detection—0
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection—0
ECATS: Explainable-by-design concept-based anomaly detection for time series—0
ECG classification using Deep CNN and Gramian Angular Field—0
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications—0
ECNN: A Low-complex, Adjustable CNN for Industrial Pump Monitoring Using Vibration Data—0
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients—0
EdgeCentric: Anomaly Detection in Edge-Attributed Networks—0
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection—0
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series—0
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data—0
Effectiveness Assessment of Recent Large Vision-Language Models—0
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles—0
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications—0
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Efficient anomaly detection using bipartite k-NN graphs—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Efficient Anomaly Detection via Matrix Sketching—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Efficient Client Selection in Federated Learning—0
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems—0
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer—0
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution—0
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data—0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Efficient Nonlinear RX Anomaly Detectors—0
Efficient pattern-based anomaly detection in a network of multivariate devices—0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Efficient Representation of the Activation Space in Deep Neural Networks—0
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM—0
EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Elsa: Energy-based learning for semi-supervised anomaly detection—0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling—0
Empirical performance maximization for linear rank statistics—0
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—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