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 2826–2850 of 4856 papers

TitleStatusHype
PSL is Dead. Long Live PSL—0
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision—0
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model—0
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning—0
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies—0
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore—0
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations—0
PV Fleet Modeling via Smooth Periodic Gaussian Copula—0
PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretation—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow—0
QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks—0
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework—0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16—0
Quantifying point cloud realism through adversarially learned latent representations—0
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology—0
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection—0
Quantum Autoencoder for Multivariate Time Series Anomaly Detection—0
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults—0
Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems—0
Quantum Machine Learning for Anomaly Detection in Consumer Electronics—0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities—0
Show:102550
← PrevPage 114 of 195Next →
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