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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1325 of 4856 papers

TitleStatusHype
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
Are Large Language Models Useful for Time Series Data Analysis?—0
A new Video Synopsis Based Approach Using Stereo Camera—0
Advances on the classification of radio image cubes—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
A Recover-then-Discriminate Framework for Robust Anomaly Detection—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A New Time Series Similarity Measure and Its Smart Grid Applications—0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection—0
Deep Learning for Medical Image Analysis—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction—0
ADTR: Anomaly Detection Transformer with Feature Reconstruction—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection—0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
A Rank-SVM Approach to Anomaly Detection—0
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid—0
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation—0
Show:102550
← PrevPage 53 of 195Next →
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