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 46514675 of 4856 papers

TitleStatusHype
leave a trace - A People Tracking System Meets Anomaly Detection0
Model Selection for Anomaly Detection0
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural NetworksCode1
Anomaly Detection and Modeling in 802.11 Wireless Networks0
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification0
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds0
Concept Drift and Anomaly Detection in Graph StreamsCode0
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups0
Interaction-Based Distributed Learning in Cyber-Physical and Social Networks0
Conformal k-NN Anomaly Detector for Univariate Data Streams0
Time Series Data Cleaning: From Anomaly Detection to Anomaly RepairingCode0
Unsupervised real-time anomaly detection for streaming dataCode0
Recurrent Estimation of Distributions0
Temporal anomaly detection: calibrating the surpriseCode0
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks0
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata0
Unmasking the abnormal events in video0
Online learnability of Statistical Relational Learning in anomaly detection0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Automatic Anomaly Detection in the Cloud Via Statistical LearningCode0
Robust, Deep and Inductive Anomaly DetectionCode0
Anomaly detection and motif discovery in symbolic representations of time series0
Grouped Convolutional Neural Networks for Multivariate Time Series0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network0
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker DiscoveryCode0
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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