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 4651–4700 of 4856 papers

TitleStatusHype
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets—0
Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer—0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Multi-Class Anomaly Detection—0
Generalized One-Class Learning Using Pairs of Complementary Classifiers—0
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation—0
Generalizing Information to the Evolution of Rational Belief—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Generating Anomalies for Video Anomaly Detection With Prompt-Based Feature Mapping—0
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection—0
Generating Synthetic X-ray Images of a Person from the Surface Geometry—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity—0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks—0
Generative Adversarial Networks and Other Generative Models—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Generative Anomaly Detection for Time Series Datasets—0
Generative Cooperative Learning for Unsupervised Video Anomaly Detection—0
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images—0
Deep Generative Design: Integration of Topology Optimization and Generative Models—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection—0
Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Genetic Neural Architecture Search for automatic assessment of human sperm images—0
GenIAS: Generator for Instantiating Anomalies in time Series—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems—0
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection—0
GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract—0
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison—0
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection—0
Global Information Guided Video Anomaly Detection—0
Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection—0
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml—0
Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware—0
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data—0
GNN-based Anomaly Detection for Encoded Network Traffic—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection—0
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition—0
Granular Learning with Deep Generative Models using Highly Contaminated Data—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View—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