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 4301–4350 of 4856 papers

TitleStatusHype
A Survey on GANs for Anomaly DetectionCode0
Detection and Statistical Modeling of Birth-Death Anomaly—0
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle—0
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network—0
AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN—0
Normalizing flows for novelty detection in industrial time series data—0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
Anomaly Detection with Joint Representation Learning of Content and Connection—0
(1 + )-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data SetsCode0
Stochastic Proximal AUC Maximization—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
Deep Learning for Spatio-Temporal Data Mining: A Survey—0
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos—0
Anomaly Detection in High Performance Computers: A Vicinity Perspective—0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
Outlier Exposure with Confidence Control for Out-of-Distribution DetectionCode0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Using anomaly detection to support classification of fast running (packaging) processes—0
Deep Semi-Supervised Anomaly DetectionCode0
An Adaptive Training-less System for Anomaly Detection in Crowd Scenes—0
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous FeaturesCode0
Detecting Anomalies in Image Classification by Means of Semantic RelationshipsCode0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Bayesian Anomaly Detection Using Extreme Value Theory—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT—0
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set—0
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder TrainingCode0
PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure—0
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators—0
Devil in the Detail: Attack Scenarios in Industrial Applications—0
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems—0
Learning Ensembles of Anomaly Detectors on Synthetic Data—0
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection—0
Online Multivariate Anomaly Detection and Localization for High-dimensional Settings—0
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection—0
Which principal components are most sensitive to distributional changes?—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Visual Analytics of Anomalous User Behaviors: A Survey—0
Using Bursty Announcements for Detecting BGP Routing Anomalies—0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches—0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
Anomaly Detection in Images—0
1D Convolutional Neural Networks and Applications: A Survey—0
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing—0
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models—0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictionsCode0
UaiNets: From Unsupervised to Active Deep Anomaly Detection—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