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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 4751–4800 of 4856 papers

TitleStatusHype
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform—0
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems—0
Anomaly Detection in Clutter using Spectrally Enhanced Ladar—0
A landmark-based algorithm for automatic pattern recognition and abnormality detection—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly DetectionCode0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
A Survey on Social Media Anomaly Detection—0
Variational Autoencoder based Anomaly Detection using Reconstruction ProbabilityCode0
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS—0
ATD: Anomalous Topic Discovery in High Dimensional Discrete DataCode0
Continuous online sequence learning with an unsupervised neural network modelCode0
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper—0
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage—0
Extracting Information from Indian First Names—0
Real-Time Anomaly Detection and Localization in Crowded Scenes—0
Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes—0
Canonical Autocorrelation Analysis—0
EdgeCentric: Anomaly Detection in Edge-Attributed Networks—0
Constant Time EXPected Similarity Estimation using Stochastic Optimization—0
ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments—0
ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments—0
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection—0
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling—0
High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies—0
Calibration of One-Class SVM for MV set estimation—0
Online Anomaly Detection via Class-Imbalance Learning—0
MultiView Diffusion Maps—0
Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysis—0
A Framework of Sparse Online Learning and Its Applications—0
On Identifying Anomalies in Tor Usage with Applications in Detecting Internet Censorship—0
Sparsity in Multivariate Extremes with Applications to Anomaly Detection—0
Learning to classify with possible sensor failures—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data—0
Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection—0
From Light to Rich ERE: Annotation of Entities, Relations, and Events—0
Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression—0
Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review—0
Long Short Term Memory Networks for Anomaly Detection in Time SeriesCode0
Robust Anomaly Detection Using Semidefinite Programming—0
Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector—0
Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation—0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Sequential Feature Explanations for Anomaly Detection—0
A Dictionary Approach to EBSD Indexing—0
Spatio-temporal Video Parsing for Abnormality Detection—0
Crowded Scene Analysis: A Survey—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
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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