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 3351–3400 of 4856 papers

TitleStatusHype
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters—0
TRACE: Time SeRies PArameter EffiCient FinE-tuning—0
Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline—0
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline—0
Tracking Real-time Anomalies in Cyber-Physical Systems Through Dynamic Behavioral Analysis—0
Tractometry-based Anomaly Detection for Single-subject White Matter Analysis—0
Traffic congestion anomaly detection and prediction using deep learning—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds—0
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models—0
Training Medical Large Vision-Language Models with Abnormal-Aware Feedback—0
Training robust anomaly detection using ML-Enhanced simulations—0
Transcriptional Response of SK-N-AS Cells to Methamidophos—0
Transfer Anomaly Detection by Inferring Latent Domain Representations—0
Transfer learning applications for anomaly detection in wind turbines—0
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—0
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Transformer-based Multivariate Time Series Anomaly Localization—0
Transformer-based normative modelling for anomaly detection of early schizophrenia—0
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos—0
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection—0
Transparent Anomaly Detection via Concept-based Explanations—0
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem—0
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components—0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning—0
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions—0
Trustworthy Anomaly Detection: A Survey—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection—0
TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights—0
TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis—0
TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision—0
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data—0
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection—0
UaiNets: From Unsupervised to Active Deep Anomaly Detection—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges—0
Ultrafast single-channel machine vision based on neuro-inspired photonic computing—0
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving—0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator—0
Uncertainty-Aware Deep Classifiers using Generative Models—0
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior—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