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 4501–4550 of 4856 papers

TitleStatusHype
Automatic Bayesian Density Analysis—0
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable FeaturesCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization—0
Comparison of RNN Encoder-Decoder Models for Anomaly Detection—0
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity—0
Are generative deep models for novelty detection truly better?Code0
Process Monitoring Using Maximum Sequence Divergence—0
Latent Space Autoregression for Novelty DetectionCode0
Anomaly Detection for Skin Disease Images Using Variational Autoencoder—0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data—0
Client-Specific Anomaly Detection for Face Presentation Attack Detection—0
How To Backdoor Federated LearningCode1
Distilling the Posterior in Bayesian Neural Networks—0
Deep One-Class ClassificationCode0
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations—0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks—0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning—0
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound DetectionCode0
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling—0
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging—0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encodersCode0
Partial AUC Maximization via Nonlinear Scoring Functions—0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving—0
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data StreamsCode0
Future Frame Prediction for Anomaly Detection – A New BaselineCode1
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems—0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder—0
Deep Anomaly Detection Using Geometric TransformationsCode1
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning—0
AVID: Adversarial Visual Irregularity DetectionCode0
Deep Active Learning for Anomaly Detection—0
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors—0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
A Compact Convolutional Neural Network for Textured Surface Anomaly DetectionCode0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs—0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep AutoencodersCode0
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic—0
Incorporating Privileged Information to Unsupervised Anomaly Detection—0
Population Anomaly Detection through Deep Gaussianization—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Dysarthric speech evaluation: automatic and perceptual approaches—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