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

Showing 45264550 of 4856 papers

TitleStatusHype
Are generative deep models for novelty detection truly better?Code0
Process Monitoring Using Maximum Sequence Divergence0
Latent Space Autoregression for Novelty DetectionCode0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data0
Anomaly Detection for Skin Disease Images Using Variational Autoencoder0
Client-Specific Anomaly Detection for Face Presentation Attack Detection0
Distilling the Posterior in Bayesian Neural Networks0
Deep One-Class ClassificationCode0
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks0
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning0
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 Modeling0
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging0
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encodersCode0
Partial AUC Maximization via Nonlinear Scoring Functions0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
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 Driving0
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data StreamsCode0
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 Systems0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified