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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 2226–2250 of 4856 papers

TitleStatusHype
Revisiting Non-separable Binary Classification and its Applications in Anomaly DetectionCode0
Bagged Regularized k-Distances for Anomaly Detection—0
Online Anomaly Detection over Live Social Video Streaming—0
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
Unsupervised textile defect detection using convolutional neural networks—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
Fast Particle-based Anomaly Detection Algorithm with Variational AutoencoderCode0
Diagnosis driven Anomaly Detection for CPS—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly DetectionCode0
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection—0
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Anomaly detection in cross-country money transfer temporal networks—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
Video Anomaly Detection using GAN—0
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators—0
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance InspectionCode0
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET—0
Correlated Attention in Transformers for Multivariate Time Series—0
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI—0
SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data—0
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—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