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 2001–2025 of 4856 papers

TitleStatusHype
Tailoring Machine Learning for Process Mining—0
FABLE : Fabric Anomaly Detection Automation ProcessCode0
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
MixedTeacher : Knowledge Distillation for fast inference textural anomaly detectionCode0
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data—0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning—0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly DetectionCode2
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection—0
Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning—0
Learning on Graphs under Label Noise—0
A Computational Theory and Semi-Supervised Algorithm for ClusteringCode0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection—0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI ForecastingCode0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering—0
Log-based Anomaly Detection based on EVT Theory with feedback—0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation—0
Permutation invariant Gaussian matrix models for financial correlation matrices—0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
A Robust Likelihood Model for Novelty Detection—0
High-dimensional and Permutation Invariant Anomaly DetectionCode0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
ReContrast: Domain-Specific Anomaly Detection via Contrastive ReconstructionCode1
Show:102550
← PrevPage 81 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