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 2326–2350 of 4856 papers

TitleStatusHype
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
A Lightweight Yet Robust Approach to Textual Anomaly Detection—0
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI—0
Graph Sanitation with Application to Node Classification—0
GraphSAC: Detecting anomalies in large-scale graphs—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Graph Pre-Training Models Are Strong Anomaly Detectors—0
Classification Tree Diagrams in Health Informatics Applications—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection—0
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve—0
Show:102550
← PrevPage 94 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