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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 2626–2650 of 4856 papers

TitleStatusHype
Brain Structure Ages -- A new biomarker for multi-disease classification—0
Exploring Diffusion Models for Unsupervised Video Anomaly Detection—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
Contrastive-Regularized U-Net for Video Anomaly Detection—0
Video Event Restoration Based on Keyframes for Video Anomaly Detection—0
AGAD: Adversarial Generative Anomaly Detection—0
KeyDetect --Detection of anomalies and user based on Keystroke Dynamics—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
What makes a good data augmentation for few-shot unsupervised image anomaly detection?—0
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management—0
Anomaly Detection via Gumbel Noise Score Matching—0
Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems—0
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection—0
PAC-Based Formal Verification for Out-of-Distribution Data Detection—0
DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from μPMU Measurement Data—0
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces—0
Unsupervised crack detection on complex stone masonry surfacesCode0
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and LocalizationCode0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers—0
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
Topological Pooling on GraphsCode0
Interpretable Anomaly Detection via Discrete Optimization—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