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 1001–1025 of 4856 papers

TitleStatusHype
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
Automated Model Selection for Time-Series Anomaly Detection—0
Anomaly Detection for an E-commerce Pricing System—0
Battery Cloud with Advanced Algorithms—0
Battery State of Health Estimation Using LLM Framework—0
Bayesian Anomaly Detection and Classification—0
A Framework of Sparse Online Learning and Its Applications—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content—0
Bayesian Hypernetworks—0
Bayesian Learning of Clique Tree Structure—0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Automated Anomaly Detection on European XFEL Klystrons—0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—0
A geometric framework for outlier detection in high-dimensional data—0
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors—0
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics—0
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder—0
Anomaly Detection Dataset for Industrial Control Systems—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—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