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 1251–1275 of 4856 papers

TitleStatusHype
Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment—0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
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
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning—0
DiffFake: Exposing Deepfakes using Differential Anomaly Detection—0
Machine Learning-Based Cloud Computing Compliance Process Automation—0
rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics—0
CyberSentinel: An Emergent Threat Detection System for AI Security—0
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
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
A Survey of Anomaly Detection in Cyber-Physical Systems—0
Statistically Significant kNNAD by Selective Inference—0
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach—0
Value Gradient Sampler: Sampling as Sequential Decision MakingCode0
Unsupervised Anomaly Detection through Mass Repulsing Optimal TransportCode0
Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC LossCode0
Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems—0
Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset—0
DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly DetectionCode0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing—0
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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