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 3276–3300 of 4856 papers

TitleStatusHype
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Adversarially learned anomaly detection for time series data—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems—0
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)—0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing—0
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder—0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making—0
A Federated Learning Approach to Anomaly Detection in Smart Buildings—0
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis—0
A flexible outlier detector based on a topology given by graph communities—0
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation—0
A framework for anomaly detection using language modeling, and its applications to finance—0
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks—0
A Framework for Verifiable and Auditable Federated Anomaly Detection—0
A Framework of Sparse Online Learning and Its Applications—0
AFSC: Adaptive Fourier Space Compression for Anomaly Detection—0
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants—0
AGAD: Adversarial Generative Anomaly Detection—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—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