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 3951–3975 of 4856 papers

TitleStatusHype
Deep Learning Models for Visual Inspection on Automotive Assembling Line—0
Feature Extraction for Novelty Detection in Network Traffic—0
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition—0
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—0
Fixing Inventory Inaccuracies At Scale—0
Traffic congestion anomaly detection and prediction using deep learning—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
G2D: Generate to Detect Anomaly—0
Lightweight Collaborative Anomaly Detection for the IoT using BlockchainCode0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
The Clever Hans Effect in Anomaly Detection—0
Analytical Probability Distributions and EM-Learning for Deep Generative Networks—0
Self-Supervised Representation Learning for Visual Anomaly Detection—0
Plug-and-Play Anomaly Detection with Expectation Maximization Filtering—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
Anomalous Motion Detection on Highway Using Deep LearningCode0
Robust Variational Autoencoder for Tabular Data with Beta Divergence—0
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior—0
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationCode0
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model—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