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 2101–2150 of 4856 papers

TitleStatusHype
A Taxonomy of Anomalies in Log Data—0
Early Anomaly Detection in Power Systems Based on Random Matrix Theory—0
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks—0
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms—0
EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns—0
A task of anomaly detection for a smart satellite Internet of things system—0
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection—0
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
Anomaly Detection and Classification in Knowledge Graphs—0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Dysarthric speech evaluation: automatic and perceptual approaches—0
ATAC-Net: Zoomed view works better for Anomaly Detection—0
DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from μPMU Measurement Data—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
Anomaly detection and classification for streaming data using PDEs—0
Dynamic Interactional And Cooperative Network For Shield Machine—0
A Systematic Mapping Study in AIOps—0
Dynamic Graph Embedding via LSTM History Tracking—0
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows—0
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network—0
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)—0
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data—0
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
Dynamic Bayesian Approach for decision-making in Ego-Things—0
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
A Survey on Time-Series Distance Measures—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
A Survey on Social Media Anomaly Detection—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Active Anomaly Detection for time-domain discoveries—0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting—0
Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection—0
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it—0
A Survey on Graph Representation Learning Methods—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
Abnormal component analysis—0
A Survey on Explainable Anomaly Detection—0
A Survey on Embedding Dynamic Graphs—0
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security—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