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 4051–4075 of 4856 papers

TitleStatusHype
Convolutional Ensembling based Few-Shot Defect Detection Technique—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors—0
Convolutional Neural Network for Multipath Detection in GNSS Receivers—0
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection—0
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection—0
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations—0
Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data—0
Copula Quadrant Similarity for Anomaly Scores—0
Coresets for Data Discretization and Sine Wave Fitting—0
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression—0
Correlated Anomaly Detection from Large Streaming Data—0
Correlated Attention in Transformers for Multivariate Time Series—0
Correlated discrete data generation using adversarial training—0
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
COVID-19 Detection Using CT Image Based On YOLOv5 Network—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos—0
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection—0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—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