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

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Showing 2701–2725 of 4856 papers

TitleStatusHype
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks—0
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection—0
Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals—0
Cellwise and Casewise Robust Covariance in High Dimensions—0
CFOF: A Concentration Free Measure for Anomaly Detection—0
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks—0
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos—0
Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning—0
Channel impulse response peak clustering using neural networks—0
Channel-wise Influence: Estimating Data Influence for Multivariate Time Series—0
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients—0
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems—0
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder—0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
CIoTA: Collaborative IoT Anomaly Detection via Blockchain—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
Classification Tree Diagrams in Health Informatics Applications—0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—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