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 1251–1300 of 4856 papers

TitleStatusHype
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
Anomaly Detection via Self-organizing MapCode0
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomographyCode0
Anomaly Detection via oversampling Principal Component AnalysisCode0
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in VideosCode0
Group Anomaly Detection using Deep Generative ModelsCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
A brief introduction to a framework named Multilevel Guidance-Exploration NetworkCode0
Anomaly Detection via Graphical LassoCode0
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing ValuesCode0
Graph Fairing Convolutional Networks for Anomaly DetectionCode0
Graph Embedded Pose Clustering for Anomaly DetectionCode0
Graph Laplacian for Image Anomaly DetectionCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly DetectionCode0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
A Compact Convolutional Neural Network for Textured Surface Anomaly DetectionCode0
Continuous online sequence learning with an unsupervised neural network modelCode0
Continual Learning Approaches for Anomaly DetectionCode0
A Deep Learning Approach for Active Anomaly Detection of Extragalactic TransientsCode0
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space ModelCode0
Good Practices and A Strong Baseline for Traffic Anomaly DetectionCode0
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly DetectionCode0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Anomaly Detection using One-Class Neural NetworksCode0
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario DetectionCode0
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect ClassificationCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel ApproachCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
Generator Based Inference (GBI)Code0
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTMCode0
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial VideosCode0
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly DetectionCode0
Anomaly Detection using Principles of Human PerceptionCode0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
gen2Out: Detecting and Ranking Generalized AnomaliesCode0
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in BusinessesCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
GANetic Loss for Generative Adversarial Networks with a Focus on Medical ApplicationsCode0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera ViewsCode0
Contrastive Language Prompting to Ease False Positives in Medical Anomaly DetectionCode0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictionsCode0
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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