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 4301–4325 of 4856 papers

TitleStatusHype
Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection—0
Distilling the Posterior in Bayesian Neural Networks—0
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design—0
Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach—0
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT—0
Distributed Deep Learning for Persistent Monitoring of agricultural Fields—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems—0
Do autoencoders need a bottleneck for anomaly detection?—0
DOC3-Deep One Class Classification using Contradictions—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Does Your Phone Know Your Touch?—0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
Domain Adaptation via Anaomaly Detection—0
Domain-Generalized Textured Surface Anomaly Detection—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
DoS and DDoS Mitigation Using Variational Autoencoders—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural 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