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 4001–4050 of 4856 papers

TitleStatusHype
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis—0
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset—0
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test—0
Compressed Smooth Sparse Decomposition—0
Compressive Change Retrieval for Moving Object Detection—0
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation—0
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey—0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Computing Graph Descriptors on Edge Streams—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets—0
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model—0
Condition monitoring and anomaly detection in cyber-physical systems—0
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss—0
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Conformalized Kernel Ridge Regression—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Constant Time EXPected Similarity Estimation using Stochastic Optimization—0
Constraining Anomaly Detection with Anomaly-Free Regions—0
Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring—0
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data—0
Constructing a meta-learner for unsupervised anomaly detection—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Context-Aware Trajectory Anomaly Detection—0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance—0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Contextual Affinity Distillation for Image Anomaly Detection—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual learning on deployment pipelines for Machine Learning Systems—0
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
Contrastive Structured Anomaly Detection for Gaussian Graphical Models—0
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies—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