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 4676–4700 of 4856 papers

TitleStatusHype
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