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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 3526–3550 of 4856 papers

TitleStatusHype
Generalized One-Class Learning Using Pairs of Complementary Classifiers—0
Task-agnostic Continual Learning with Hybrid Probabilistic Models—0
A new Video Synopsis Based Approach Using Stereo Camera—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
Detecting Anomalous User Behavior in Remote Patient Monitoring—0
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis—0
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression—0
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time SeriesCode0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
Large-Scale Network Embedding in Apache Spark—0
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison—0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions—0
Anomaly Detection and Automated Labeling for Voter Registration File Changes—0
X-MAN: Explaining multiple sources of anomalies in video—0
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks—0
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks—0
Inverting Adversarially Robust Networks for Image SynthesisCode0
Time Series Anomaly Detection with label-free Model Selection—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
Unsupervised Anomaly Detection Ensembles using Item Response TheoryCode0
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Predicting Next Local Appearance for Video Anomaly DetectionCode0
GBHT: Gradient Boosting Histogram Transform for Density Estimation—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