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 376–400 of 4856 papers

TitleStatusHype
Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining—0
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network—0
Identification and Characterization for Disruptions in the U.S. National Airspace System (NAS)—0
Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment—0
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns—0
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly DetectionCode1
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
Machine Learning-Based Cloud Computing Compliance Process Automation—0
DiffFake: Exposing Deepfakes using Differential Anomaly Detection—0
rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics—0
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly DetectionCode2
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
CyberSentinel: An Emergent Threat Detection System for AI Security—0
dtaianomaly: A Python library for time series anomaly detectionCode2
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion—0
Flow-based generative models as iterative algorithms in probability space—0
Unsupervised Anomaly Detection through Mass Repulsing Optimal TransportCode0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly DetectionCode2
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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